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Open Access

Peer-reviewed

Research Article

Taxonomy-based content analysis of sedentary behavior questionnaires: A systematic review

  • Fabien Rivière,

    Roles Formal analysis, Methodology, Writing – original draft, Writing – review & editing

    Affiliation EA 4360 APEMAC, University of Lorraine, Paris Descartes University, Nancy, France

    ⨯
  • Salomé Aubert,

    Roles Formal analysis, Methodology, Writing – review & editing

    Affiliation Healthy Active Living and Obesity Research, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Canada

    ⨯
  • Abdou Yacoubou Omorou,

    Roles Formal analysis, Methodology, Writing – review & editing

    Affiliations EA 4360 APEMAC, University of Lorraine, Paris Descartes University, Nancy, France, INSERM, CIC-1433 Clinical Epidemiology, CHRU Nancy, France

    ⨯
  • Barbara E. Ainsworth,

    Roles Formal analysis, Methodology, Writing – review & editing

    Affiliation College of Health Solutions, Arizona State University, Phoenix, AZ, United States of America

    ⨯
  • Anne Vuillemin

    Roles Formal analysis, Methodology, Writing – review & editing

    anne.vuillemin@unice.fr

    Affiliations EA 4360 APEMAC, University of Lorraine, Paris Descartes University, Nancy, France, Université Côte d’Azur, LAMHESS, Nice, France

    ⨯

Abstract

Background

Health effects of sedentary behaviors (SB) may vary depending on their characteristics such as type, purpose, duration, and intensity of the behavior. While a growing number of questionnaires assess sedentary behaviors, it is unclear which characteristics of SB are measured. The aim of this review was to examine the content of self-report SB questionnaires.

Methods

Three databases were searched for sedentary behavior questionnaires published before January 1st, 2016. Based on the inclusion criteria, 82 articles out of 1369 were retrieved for a total of 60 questionnaires. For each questionnaire, the sedentary behavior characteristics identified were reported and analyzed.

Results

Most of the questionnaires assessed the time (n = 60), posture (n = 54), purpose (n = 46) and the types (n = 45) of SB performed. Fewer questionnaires assessed the environment (n = 20) social context (n = 11), status (n = 2), and associated behaviors (n = 2) related to sedentary behaviors. All the questionnaires except two assessed time spent in SB with 17 assessing frequency and 6 assessing breaks in SB. The most frequent characteristics identified in the questionnaires were the categories of sitting (90%), a day (95%), watching television (65%) and using a computer (55%). Many characteristics of SB were not measured.

Conclusions

By knowing the breadth of SB included in questionnaires, this review provides support to shape the design of new questionnaires designed to reduce the gaps in measuring sedentary behaviors.

Citation: Rivière F, Aubert S, Omorou AY, Ainsworth BE, Vuillemin A (2018) Taxonomy-based content analysis of sedentary behavior questionnaires: A systematic review. PLoS ONE 13(3): e0193812. https://doi.org/10.1371/journal.pone.0193812

Editor: Maciej Buchowski, Vanderbilt University, UNITED STATES

Received: May 4, 2017; Accepted: February 19, 2018; Published: March 6, 2018

Copyright: © 2018 Rivière et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Sedentary behaviors (SB) are defined as “as any waking behavior characterized by an energy expenditure ≤1.5 METs while in a sitting or reclining posture” [1]. Health effects of sedentary time have been studied over the past decade with most studies showing negative associations between sedentary time and health outcomes in both adults and youth [2–4]. Much of the evidence for these results has been provided by self-report [2] with the majority of the studies measuring television (TV) viewing or total sitting time derived from a single question [4,5]. However, measuring only total sedentary time may not provide enough information to understand the health effects of SB. For example, an individual can engage in different types of SB including TV viewing, using a computer, reading, writing, and eating which have several purposes including work, transportation, and leisure time. The types and purposes of SB will differ for each person studied. Some studies have shown that the associations between SB and health-related outcomes vary by the characteristics of the SB measured and in the manner in which sedentary time is accumulated [6–8]. A systematic review of the effects of SB on health outcomes showed that TV viewing had a different impact than reading on cognitive development in early childhood [9]. The investigators showed detrimental associations between the total duration and frequency of watching TV and videos and using computers and/or overall screen time with cognitive development, whereas beneficial associations were found between the total duration and frequency of reading or being read to and cognitive development. However, the associations were complex as positive associations were shown for some TV content (educational channel viewing) while negative associations were observed for other content (cartoons). These findings are supported by another systematic review examining the relationships between SB and health indicators in children and youth [10] that showed negative associations between screen-related behaviors with body composition and cardiometabolic status (TV viewing), behavioral conduct and pro-social behavior (TV viewing and video game use), physical fitness (screen time), and self-esteem (screen time and computer use). Conversely, increased duration of reading and doing homework were associated with higher academic achievement. Such relationships imply the association between SB and health outcomes is complex and that multiple characteristics of SB should be taken into consideration in research studies. Therefore, measuring the characteristics of SB is important as it may allow researchers to understand factors mediating the relationships between sedentary time and various health-outcomes, reveal insights into an individual’s behavior, relationships between various determinants and correlates of health outcomes, and implement efficient interventions to reduce SB.

As SB are complex behaviors, their assessment is a challenge. Methods used to measure SB include subjective instruments, including questionnaires, logs, and ecological momentary assessment (EMA). Objective instruments include motion- and posture sensors. Subjective instruments are used to collect qualitative information about SB including the types and purposes of SB. Because of the ease of use and low burden, questionnaires often are used to recall detailed information about SB. To advance knowledge of the effects of SB on health outcomes, it is important that multiple characteristics of SB can be assessed by questionnaires.

To better characterize SB, a taxonomy of SB was developed by Chastin and colleagues in 2013 [11]. The taxonomy of SB was the result of the first round of an open science project referred to as SIT, a term used to represent the Consensus Taxonomy of Sedentary Behaviors. This formal consensus process involved international experts who offered a comprehensive frame of reference for SB developed through a Delphi method to identify components of SB. The resulting taxonomy includes nine complementary categories (referred to as facets) and sub-categories to describe SB: posture (sitting or lying), the purpose of the behavior (such as work or for transportation), the time of day or year when one engages in SB, the types of behaviors engaged in while sedentary (no screen or screen time), the environment (community, physical, and location) and social context (alone or with others) where SB occurred, the associated behaviors (such as eating and smoking), one’s status (relating to function and psychology), and the instruments measuring the behavior (subjective, objective, and metrics) (see Figs 1 and 2 for an example of the taxonomy of SB). Questionnaires assessing SB vary considerably in length and item content. While the measurement properties of SB questionnaires have been assessed in several reviews [12,13], examination of the content of SB questionnaires in a detailed and standardized manner is warranted. Therefore, the aim of this study was to use the Taxonomy of SB to systematically appraise and compare the content of SB questionnaires. We provide information regarding the facets and categories of SB measured in published questionnaires. This information has the potential to support the development of new questionnaires that measure SB characteristics not measured currently and to reduce the gaps in measuring SB.

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Fig 1. Taxonomy level one.

https://doi.org/10.1371/journal.pone.0193812.g001

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Fig 2. Taxonomy level one purpose and sublevels.

https://doi.org/10.1371/journal.pone.0193812.g002

The objectives of this study were (1) to examine the content of questionnaires measuring SB and identify the indicators used to synthetize the information recorded, and (2) to compare the content of the questionnaires based on a well-defined and standardized classification of SB.

Methods

This systematic review aimed to identify all studies published before January 1, 2016 that report the development and/or the psychometric properties of self-report questionnaires to assess SB. The PRISMA Statement was used to guide the report of this work [14]. The PRISMA checklist is available in the supporting information (see S1 Table).

Literature search

The following electronic bibliographic databases were searched: Medline (PubMed), PsycINFO/ARTICLE (EBSCOhost) and SportDiscus (EBSCOhost). The full search strategies in (A) PubMed and (B) PsycINFO/ARTICLE and SportDiscus were as follows:

  1. (A). (sedentar*[TIAB] OR Sedentary Lifestyles[MeSH] OR sitting[TIAB]) AND (questionnaires[MeSH] OR questionnaire*[TIAB] OR report*[TIAB]) AND (valid*[TIAB] OR reliab*[TIAB] OR Reproducibility of Results[MeSH])
  2. (B). (TI(sedentar* OR sitting) OR AB(sedentar* OR sitting)) AND (TI(questionnaire* OR report*) OR AB(questionnaire* OR report*)) AND (TI(valid* OR reliab*) OR AB(valid* OR reliab*))

In addition, existing reviews of SB questionnaires were hand-searched to identify potential missing questionnaires [11, 12].

Study inclusion and exclusion criteria

Studies meeting all of the inclusion criteria were included: (i) the aim of the study was the development of a measurement instrument or the evaluation of one or more of its measurement properties; (ii) the instrument under study was self-reported, either self-administered or administered by a researcher in the form of an interview. Proxy-reported questionnaires were excluded (proxy questionnaires are used to measure the characteristics of a subject by asking other people close to the subject such as the parents or caregiver); (iii) the instrument was a questionnaire. Use-of-time tools, logs and diaries were excluded; (iv) the questionnaire measured SB; (v) the study was accepted as a full text original article in a peer-reviewed journal until December 31, 2015; (vi) the article was published in English or French and the questionnaire was available in one of these languages.

Study selection

Two reviewers independently assessed titles/abstracts (AV, FR) and selected full-text articles (FR, SA) based upon the eligibility criteria. In the case of a disagreement between the two reviewers, a third reviewer (AO) made the final decision. Full text copies were obtained for all but three articles meeting initial screening by one of the reviewer (FR). The reviewers were not blinded to the authors or journals when extracting data.

Data extraction

Description of questionnaires.

The general characteristics of the instruments were extracted from the papers using a standardized data-extraction form. This information included: (i) name of the questionnaire; (ii) version; (iii) construct to be measured; (iv) targeted age group; (v) number of items; (vi) mode of administration; (vii) recall period; (viii) dimensions; and (ix) indicators. Two reviewers independently extracted all the data. Disagreement were resolved through discussion and consensus.

Content of questionnaires.

The content comparison aimed to identify the SB characteristics measured by each questionnaire for each item. To allow the comparison and analysis of the questionnaires, the decision was made to link the SB characteristics measured to the taxonomy of SB [13]. The taxonomy served as a reference framework to identify and classify the different categories of SB. The taxonomy of SB is composed of nine main facets (Fig 1). Each of the facets have sub-categories. For example, the level one facet “purpose” has three sub-categories (referred to as sublevel facets) as presented in Fig 2. The content of each questionnaire was systematically linked to the corresponding facets and sub-categories of the taxonomy of SB following standardized linking rules (see Table 1). A short-hand version of the taxonomy of SB was used to reduce the ambiguity of the results of the linking process by omitting “undetermined” and “others” categories. To allow the linking process, the taxonomy was used in a hierarchical structure. For each questionnaire, the following information was reported: (i) the number of items assessing SB characteristics; (ii) the number of SB characteristics identified; and (iii) the facets and categories of the taxonomy covered.

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Table 1. Guidelines for linking questionnaires’ items to the taxonomy of SB.

https://doi.org/10.1371/journal.pone.0193812.t001

The linking process was inspired from the International Classification of Functioning, Disability and Health linking rules [15] and adapted to this purpose. The linking rules were developed first and then refined after being applied to some questionnaires. The final linking rules comprised of eight rules as listed in Table 1. The linking process was performed by two independent researchers who were trained in applying the taxonomy and the linking rules. Disagreement between the independent ratings was discussed until a consensus was reached.

Results

The literature search

The literature search produced a total of 1,369 hits: 946 in PubMed, 221 in PsycINFO/ARTICLES and 202 in SportDiscus. When selecting articles based on the inclusion criteria, 82 studies were retrieved and three additional articles were identified based on hand-searching of existing reviews for a total of 60 questionnaires. The retrieval process and the full list of questionnaire abbreviations and their corresponding definitions are presented in Fig 3 and S2 Table, respectively.

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Fig 3. Flow chart.

https://doi.org/10.1371/journal.pone.0193812.g003

Description of questionnaires

A description of the selected questionnaires describing SB item-characteristics is presented in Table 2. Some questionnaires included items only on SB and other questionnaires included items about SB and PA. When the questionnaires measured PA, only the SB-related content was abstracted and reviewed. From the 60 questionnaires meeting the inclusion criteria, 24 measured SB only and 36 measured both SB and PA. Questionnaires were developed and/or tested for use in the following populations: healthy adults (n = 33), adults with specific health problems (n = 11), adolescents (n = 9), seniors (n = 9), children (n = 3), women (n = 1), and students (n = 1). The majority were self-administered (n = 49) and the others were interviewer-administered (n = 25). The recall period was either a single day (n = 23) including the previous day, workday, or week-end day, a week (n = 28) including a usual week or last week, past month (n = 7), or a longer recall period (n = 6). All the questionnaires, except two which were not defined, assessed time spent in SB in hours or minutes. Seventeen questionnaires measured the frequency of SB using various metrics and six measured breaks in SB.

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Table 2. Description of sedentary behaviors items from published questionnaires.

https://doi.org/10.1371/journal.pone.0193812.t002

Taxonomy-based content analysis

Overall, 567 SB characteristics were identified and linked to the taxonomy. The questionnaire content is presented in a shortened taxonomy format in Table 3 and is presented in the full taxonomy format in S3 Table. Important differences were observed in the characteristics of the SB measured. Among the 60 questionnaires reviewed, SB facets observed in descending order of frequency were: time (n = 60), posture (n = 54), purpose (n = 46), type (n = 45), environment (n = 20), social context (n = 11), status (n = 2) and associated behaviors (n = 2). The mean number of items per questionnaire was 14.2 [min–max = 1–115] and the mean number of SB characteristics measured per questionnaire was 9.5 [min–max = 2–27]. For questionnaires measuring only SB, the mean number of SB characteristics per questionnaire was 11.7 [min–max = 2–27] and questionnaires measuring both PA and SB the mean number was 8.1 [min–max = 2–23]. The most frequent SB characteristics in the questionnaires were time (in a day, 95%), posture (sitting, 90%), and type (TV, 65%; computer, 55%). Conversely, some SB characteristics were not measured including associated behaviors and most of the sub-categories for environment and status facets. Among the questionnaires reviewed, the ASAQ, SIT-Q-12m, SIT-Q-7d and STAR-Q were the most comprehensive. They included 55–115 items that measured 13–27 SB characteristics. The least comprehensive questionnaires were CSIST, IPAQ-SF and GPAQ which had only one item measuring overall sitting time. Table 3 presents a comprehensive evaluation of the taxonomy’s facets contained in each of the reviewed SB questionnaire items. The column labeled Taxonomy presents the main facets (bolded) followed by the first level of their associated sub-categories. The letters and numbers to the left of the facets reflect the system used to classify the facet and sub-categories. The facet titled measurement is omitted since all instruments were self-report questionnaires. The names of the questionnaires reviewed are abbreviated in the top row. The X and (X) symbols identify when the facets and/or sub-categories are measured by a questionnaire and when an example is given for a SB facet and/or sub-category in the questionnaire, respectively.

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Table 3. Questionnaires ‘content linked to the taxonomy.

https://doi.org/10.1371/journal.pone.0193812.t003

Discussion

The aim of this review was to examine and compare the content of questionnaires measuring SB using facets and sub-categories of SB as described in Chastin et al.’s Taxonomy of SB. Overall, our review reports wide differences in the questionnaires’ content with the most comprehensive questionnaires measuring up to 27 SB characteristics while the least comprehensive questionnaires measured only one characteristic, overall sitting time. Most of the questionnaires measured sitting time spent watching TV or using a computer during a day. Since studies show that screen-related SB may be associated differently with health-related outcomes than other types of SB [10, 11], one should determine which characteristics of SB are of interest when selecting a questionnaire.

Questionnaires developed to obtain a more comprehensive measurement of SB characterizes patterns of SB during daily life by measuring more of the facets and categories in the taxonomy than less comprehensive questionnaires. The more comprehensive questionnaires allow consideration of a variety of SB when exploring relationships of SB to health outcomes. Many comprehensive questionnaires, such as the SIT-Q, the MPAQ and the STAR-Q, are structured into different sections whereby each section represents a purpose. For each purpose, the questionnaire asks about the time spent in SB or a characteristics of the SB. As an example, the SIT-Q-7d is one of the more comprehensive SB questionnaires. It consists of 68 items and measures time spent in different SB for work, transportation, domestic, education, socializing, eating and caregiving settings during a week day and a week-end day. This kind of structure is beneficial when addressing the complexity of SB. Not all facets were measured consistently. In some questionnaires, the purposes of SB performed during leisure activities were identified with follow-up questions, yet for work activities, only the overall sitting time was measured. Furthermore, some categories under the purpose facet were measured incompletely while other categories had several follow-up items. Only four questionnaires asked about caregiving and/or domestic SB, whereas 21 questionnaires asked about work SB and 19 questionnaires asked about leisure-time SB.

Other facets of SB were seldom measured by SB questionnaires including associated behaviors (queried as “what else?”), the social context (with whom?), and the status of an individual. These characteristics may be of interest to researchers as they have the potential to introduce bias in the relationship between SB and health-related outcomes. Associated behaviors, such as eating while watching TV, are associated with an increased risk of obesity [97] possibly resulting from nutritionally poor food choices influenced by TV commercials, less feeling of satiety while distracted by TV viewing, or by the replacement of physical activity by a sedentary behavior [98]. The social context seldom is considered when investigating SB and health outcomes. Both the quantity (having many social relationships vs. their relative absence) and quality (types of emotional support or conflict from others) of social relationships are associated with morbidity and mortality [99]. Thus, it can be expected that the social context during SB can influence the strength of the association between SB and health-related outcomes. Further, SB while alone may place one at a greater risk of health complications than engaging in SB with others. The facets of environment and time identify where a SB occurred and how long a SB occurred, respectively. These facets have a limited number of sub-categories. For the environment facet, the sub-category of indoor SB behaviors is measured on many SB questionnaires. The time facet includes two categories relating to SB performed during a day and a year. While time of the year (seasons) is known to affect PA, little is known about how it influences SB. Similarly, the environment has been identified as one of the main determinants of SB [100], however little information is available about the natural and built environment in which an individual engages in SB. (S3 Table).

Only two questionnaires asked about multitasking as associated behaviors. Individuals can engage in several tasks simultaneously, such as watching TV and chatting via Skype or Facebook or other behaviors. Watching TV could be associated with negative cognitive outcomes of using screen-based devices to chat with friends if it impacts poorly on well-being and self-esteem [101]. Little is known about how sedentary multitasking might pose a health risk as multitasking can have both distinct positive and negative health outcomes. It has been suggested that multitasking activities are associated with an increase in negative emotions, stress, psychological distress, and work-family conflict in women [102] and that media multitasking could be a unique risk factor for mental health problems [103]. Understanding the association between media use and mental health needs to consider the types of media people use, how they engage with the media, and the content of the media. Collectively, these concerns support the need to measure multitasking when investigating health effects of SB.

The taxonomy-based content analysis also brings to light that some of the characteristics of SB measured in many questionnaires that did not appear in the sub-categories of the taxonomy such as doing arts and crafts, talking with acquaintances, and hobbies. While it is not possible to add all SB characteristics to the taxonomy, identifying important characteristics common to many research settings could enrich the existing taxonomy. Even though SB is defined as any waking behavior characterized by an energy expenditure ≤1.5 METs while in a sitting or reclining posture [1], sleeping and taking a nap are classified as SB in the taxonomy. Similarly, a few characteristics of SB presented in the taxonomy are classified as physical activity on some questionnaires. In particular, cooking and household chores are included as a sub-category under the no-screen sub-category in the taxonomy. Based on the 2011 Adult Compendium of Physical Activities, these behaviors are assigned MET values > 1.5 and are scored as light-intensity activities in some questionnaires [104]. The sub-category making music could be classified as either a SB or a light-intensity physical activity depending on the questionnaire used. Yoga relaxation was classified as a SB by one questionnaire while its associated energy expenditure is 2.0 METs in the 2011 Adult Compendium of Physical Activities. The Taxonomy of SB and most of studies reviewed classified time spent in front of small screen devices such as a phone or music player as a SB; however, the energy cost of these devices can increase while walking or standing as seen with the mobile application Pokémon Go. Thus, asking for the posture of one’s SB would be useful to clarify the types of SB performed. These caveats aside, the boundary between SB and light-intensity physical activity is small and complex. Clarification of what constitutes a SB has reflected changes in the definition of SB over time. Given that the measurement and epidemiology of SB is a relatively new research field, efforts must be taken to harmonize and standardize the measurement of SB.

Differences in the recall frame, duration and mode of administration were observed in the SB questionnaires reviewed in this study. The most common recall frames were one week and/or one day which reflect the efficacy of short recall periods in enhancing the recall of information [105]. Longer recall frames are able to measure usual patterns of SB, however the potential for recall bias also is greater than for shorter recall periods [12]. All but two questionnaires measured time spent in SB. Depending on the questionnaire, duration was recalled either in hours and/or minutes per day as a continuous variable or in hours and/or minutes per day as a discrete variable. Among the questionnaires reviewed, 49 were validated using a self-administered paper or computer format and 25 were evaluated using an interview-administered in a face-to-face or telephone format. The mode of administration of questionnaires is important to reduce social desirability bias [106]. While this study included self-reported questionnaires only, proxy-report may be more appropriate for use in populations with limited cognitive capacities (i.e., children, intellectually-disabled persons, and older adults) due to their inability to recall the details of the questionnaire. In that case, parents, relatives or professional health care proxy reports may be appropriate to collect questionnaire information about the participant’s SB [5].

Limitations

The use of the Taxonomy of Sedentary Behaviors to analyze the content of the questionnaires is a long and tedious process. Some SB characteristics appeared twice in the taxonomy and other characteristics had similar wording (i.e., at the workplace and for work) making the linking process difficult. The development of linking rules was an essential step to ensure that all of the questionnaires’ content was linked following the same criteria. Despite the linking rules, some content was linked differently between the two reviewers with a consensus reached after discussion. The use of the taxonomy served as a reference framework to allow a standardized comparison of the questionnaires’ content. Further, since only articles written in English and in French were reviewed and no grey literature was searched, we can’t rule out the possibility that some SB questionnaires were omitted.

Conclusions

This study presented a standardized content analysis of 60 SB questionnaires to show the number and type of characteristics of the Taxonomy of SB measured in each questionnaire. Considerable variability was observed in the comprehensiveness of SB in the questionnaires reviewed. Questionnaires ranged from 1–115 items measuring from 2–27 SB characteristics. Facets for time, posture, purpose, and type were measured most often and facets for status and associated behaviors were measured least often. Sitting, TV viewing, and computer use were observed most often. A per day recall period was most frequent. When selecting a SB questionnaire, one should consider the measurement properties, the characteristics of SB, and the nature of information about the frequency, duration, interruptions, and recall frame. The taxonomy-based content analysis provides a useful tool to identify and compare the content of SB questionnaires as it provides a framework of SB characteristics with which to evaluate questionnaires. This review provides support for the development of questionnaires that measure SB characteristics not currently measured in existing questionnaires. These include associated behaviors performed in sedentary time, multitasking, physical and social environments, locations of SB, and the functional and psychological status of individuals performing the behaviors.

Supporting information

S1 Table. PRISMA checklist.

https://doi.org/10.1371/journal.pone.0193812.s001

(DOC)

S2 Table. Full list of questionnaire abbreviations and their corresponding definitions.

This file presents the entire list of SB questionnaires analyzed in this review, their abbreviations, and the references for each of them.

https://doi.org/10.1371/journal.pone.0193812.s002

(DOCX)

S3 Table. Content of sedentary behaviors questionnaires.

This table presents in the column A and B the short form of the taxonomy in a hierarchical form. In the other columns are presented the SB characteristics identified within each questionnaire. The SB characteristics linked to the taxonomy are represented by an X, while the (X) represents the SB characteristics explained by examples (cf. linking rules).

https://doi.org/10.1371/journal.pone.0193812.s003

(XLSX)

Acknowledgments

The authors thank the individuals who have shared their questionnaires.

References

  1. 1. Sedentary Behaviour Research Network. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours.” Appl Physiol Nutr Metab. 2012;37(3):540–2. pmid:22540258
  2. 2. Biswas A, Oh PI, Faulkner GE, Bajaj RR, Silver MA, Mitchell MS, et al. Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis. Ann Intern Med. 2015;162(2):123–132. pmid:25599350
  3. 3. Chau JY, Grunseit AC, Chey T, Stamatakis E, Brown WJ, Matthews CE, et al. Daily sitting time and all-cause mortality: a meta-analysis. PLoS ONE. 2013;8(11):e80000. pmid:24236168
  4. 4. Tremblay MS, LeBlanc AG, Kho ME, Saunders TJ, Larouche R, Colley RC, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act. 2011; 8(1):98. http://dx.doi.org/10.1186/1479-5868-8-98
  5. 5. Atkin AJ, Gorely T, Clemes SA, Yates T, Edwardson C, Brage S, et al. Methods of Measurement in epidemiology: sedentary Behaviour. Int J Epidemiol. 2012;41(5):1460–1471. pmid:23045206
  6. 6. Healy GN, Dunstan DW, Salmon J, Cerin E, Shaw JE, Zimmet PZ, et al. Breaks in sedentary time beneficial associations with metabolic risk. Diabetes care. 2008;31(4):661–666. pmid:18252901
  7. 7. Saunders TJ, Tremblay MS, Mathieu M-È, Henderson M, O'Loughlin J, Tremblay A, et al. Associations of sedentary behavior, sedentary bouts and breaks in sedentary time with cardiometabolic risk in children with a family history of obesity. PLoS ONE. 2013;8(11):e79143. pmid:24278117
  8. 8. Kikuchi H, Inoue S, Sugiyama T, Owen N, Oka K, Nakaya T, et al. Distinct associations of different sedentary behaviors with health-related attributes among older adults. Prev Med. 2014;67:335–339. pmid:25117527
  9. 9. Carson V, Kuzik N, Hunter S, Wiebe SA, Spence JC, Friedman A, et al. Systematic review of sedentary behavior and cognitive development in early childhood. Preventive Medicine. 2015;78:115–122. pmid:26212631
  10. 10. Carson V, Hunter S, Kuzik N, Gray CE, Poitras VJ, Chaput JP, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth: an update. Appl Physiol Nutr Metab 2016; 41(6):S240–S265. http://dx.doi.org/10.1139/apnm-2015-0630
  11. 11. Chastin SFM, Schwarz U, Skelton DA. Development of a Consensus Taxonomy of Sedentary Behaviors (SIT): Report of Delphi Round 1. PLoS ONE. 2013;8(12):e82313. pmid:24312653
  12. 12. Clark BK, Sugiyama T, Healy GN, Salmon J, Dunstan DW, Owen N. Validity and reliability of measures of television viewing time and other non-occupational sedentary behaviour of adults: a review. Obesity Reviews. 2009;10(1):7–16. pmid:18631161
  13. 13. Healy GN, Clark BK, Winkler EAH, Gardiner PA, Brown WJ, Matthews CE. Measurement of Adults’ Sedentary Time in Population-Based Studies. Amn J Preve Med. 2011;41(2):216–227. http://dx.doi.org/10.1016/j.amepre.2011.05.005.
  14. 14. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 2009;6(7):e1000097. pmid:19621072
  15. 15. Cieza A, Geyh S, Chatterji S, Kostanjsek N, Ustün B, Stucki G. ICF linking rules: an update based on lessons learned. J Rehabil Med. 2005;37(4):212–218. pmid:16024476
  16. 16. Bonn SE, Bergman P, Trolle Lagerros Y, Sjolander A, Balter K. A Validation study of the Web-Based Physical Activity Questionnaire Active-Q Against the GENEA Accelerometer. JMIR Res Protoc 2015; 4(3):e86. pmid:26183896
  17. 17. Clark BK, Lynch BM, Winkler EA, Gardiner PA, Healy GN, Dunstan DW et al. Validity of a multi-context sitting questionnaire across demographically diverse population groups: AusDiab3. Int J Behav Nutr Phys Act. 2015;12:148. pmid:26637392
  18. 18. Aadahl M, Jorgensen T. Validation of a New Self-Report Instrument for Measuring Physical Activity: Medicine & Science in Sports & Exercise. 2003;35(7):1196–1202. http://dx.doi.org/10.1249/01.MSS.0000074446.02192.14.
  19. 19. de Fátima Guimarães R, Pereira da Silva M, Legnani E, Mazzardo O, de Campos W. Reproducibility of adolescent sedentary activity questionnaire (ASAQ) in Brazilian adolescents. Brazilian Journal of Kineanthropometry & Human Performance. 2013;15(3):276–285. http://dx.doi.org/10.5007/1980-0037.2013v15n3p276.
  20. 20. Hardy LL, Booth ML, Okely AD. The reliability of the Adolescent Sedentary Activity Questionnaire (ASAQ). Prev Med. 2007;45(1):71–74. pmid:17532371
  21. 21. Aguilar-Farías N, Brown WJ, Olds TS, (Geeske) Peeters GMEE. Validity of self-report methods for measuring sedentary behaviour in older adults. J Sci Med Sport. 2015;18(6):662–6. pmid:25172367
  22. 22. Chinapaw MJ, Slootmaker SM, Schuit AJ, van Zuidam M, van Mechelen W. Reliability and validity of the Activity Questionnaire for Adults and Adolescents (AQuAA). BMC Med Res Methodol. 2009;9(1):58. http://dx.doi.org/10.1186/1471-2288-9-58.
  23. 23. Oostdam N, van Mechelen W, van Poppel M. Validation and responsiveness of the AQuAA for measuring physical activity in overweight and obese pregnant women. Journal of Science and Medicine in Sport. 2013;16(5):412–416. pmid:23063355
  24. 24. Fjeldsoe B, Marshall A, Miller Y. Measurement properties of the Australian women’s activity survey. Med Sci Sports Exerc. 2009;41(5):1020–1033. pmid:19346985
  25. 25. Strugnell C, Renzaho A, Ridley K, Burns C. Reliability of the modified child and adolescent physical activity and nutrition survey, physical activity (CAPANS-PA) questionnaire among chinese-australian youth. BMC Med Res Methodol. 2011;11(1):122. http://dx.doi.org/10.1186/1471-2288-11-122.
  26. 26. Gennuso KP, Matthews CE, Colbert LH. Reliability and Validity of Two Self-Report Measures to Assess Sedentary Behavior in Older Adults. J Phys Act Health. 2015;12(5):727–32. pmid:25110344
  27. 27. Clemes SA, David BM, Zhao Y, Han X, Brown W. Validity of two self-report measures of sitting time. J Phys Act Health. 2012;9(4):533–539. pmid:21946087
  28. 28. Schmitz KH, Harnack L, Fulton JE, Jacobs DR, Gao S, Lytle LA, et al. Reliability and validity of a brief questionnaire to assess television viewing and computer use by middle school children. J Sch health. 2004;74(9):370–377. pmid:15656264
  29. 29. Wareham NJ, Jakes RW, Rennie KL, Mitchell J, Hennings S, Day NE. Validity and repeatability of the EPIC-Norfolk Physical Activity Questionnaire. Int J Epidemiol. 2002;31(1):168–174. pmid:11914316
  30. 30. Cleland CL, Hunter RF, Kee F, Cupples ME, Sallis JF, Tully MA. Validity of the Global Physical Activity Questionnaire (GPAQ) in assessing levels and change in moderate-vigorous physical activity and sedentary behaviour. BMC Public Health. 2014;14(1):1255. http://dx.doi.org/10.1186/1471-2458-14-1255.
  31. 31. Herrmann SD, Heumann KJ, Ananian CA Der, Ainsworth BE. Validity and Reliability of the Global Physical Activity Questionnaire (GPAQ). Meas Phys Educ Exerc Sci. 2013;17(3):221–235. http://dx.doi.org/10.1080/1091367X.2013.805139
  32. 32. Hoos T, Espinoza N, Marshall S, Arredondo EM. Validity of the global physical activity questionnaire (GPAQ) in adult Latinas. J Phys Act Health. 2012;9(5):698. pmid:22733873
  33. 33. Cerin E, Sit CHP, Huang Y-J, Barnett A, Macfarlane DJ, Wong SSH. Repeatability of self-report measures of physical activity, sedentary and travel behaviour in Hong Kong adolescents for the iHealt(H) and IPEN—Adolescent studies. BMC Pediatr. 2014;14:142. pmid:24903156
  34. 34. Tran DV, Lee AH, Au TB, Nguyen CT, Hoang DV. Reliability and validity of the International Physical Activity Questionnaire–Short Form for older adults in Vietnam. Health Promot J Austr. 2013;24(2):126. pmid:24168739
  35. 35. Cerin E, Barnett A, Cheung M, Sit CH, Macfarlane DJ, Chan W. Reliability and validity of the IPAQ-L in a sample of Hong Kong urban older adults: does neighborhood of residence matter? J Aging Phys Act. 2012;20(4):402–420.35. pmid:22186607
  36. 36. Chastin SFM, Culhane B, Dall PM. Comparison of self-reported measure of sitting time (IPAQ) with objective measurement (activPAL). Physiol Meas. 2014;35(11):2319–2328. pmid:25341050
  37. 37. Crinière L, Lhommet C, Caille A, Giraudeau B, Lecomte P, Couet C, et al. Reproducibility and validity of the French version of the long International Physical Activity Questionnaire in patients with type 2 diabetes. J Phys Act Health. 2011;8(6):858. pmid:21832302
  38. 38. Hagstromer M, Ainsworth BE, Oja P, Sjostrom M. Comparison of a subjective and an objective measure of physical activity in a population sample. J Phys Act Health. 2010;7(4):541.38. pmid:20683097
  39. 39. Hansen AW, Dahl-Petersen I, Helge JW, Brage S, Grønbæk M, Flensborg-Madsen T. Validation of an Internet-based long version of the International Physical Activity Questionnaire in Danish adults using combined accelerometry and heart rate monitoring. J Phys Act Health. 2014;11(3):654–664. pmid:23416716
  40. 40. Roman-Viñas B, Serra-Majem L, Hagströmer M, Ribas-Barba L, Sjöström M, Segura-Cardona R. International Physical Activity Questionnaire: Reliability and validity in a Spanish population. European Journal of Sport Science. 2010;10(5):297–304. http://dx.doi.org/10.1080/17461390903426667.
  41. 41. Rosenberg DE, Bull FC, Marshall AL, Sallis JF, Bauman AE. Assessment of sedentary behavior with the International Physical Activity Questionnaire. J Phys Act Health. 2008;5:S30. pmid:18364524
  42. 42. Rosenberg DE, Norman GJ, Wagner N, Patrick K, Calfas KJ, Sallis JF. Reliability and validity of the Sedentary Behavior Questionnaire (SBQ) for adults. J Phys Act Health. 2010;7(6):697–705. pmid:21088299
  43. 43. Van Dyck D, Cardon G, Deforche B, De Bourdeaudhuij I. IPAQ interview version: convergent validity with accelerometers and comparison of physical activity and sedentary time levels with the self-administered version. J Sports Med Phys Fitness. 2015; 55(7–8):776–86. pmid:24921615
  44. 44. Oyeyemi AL, Bello UM, Philemon ST, Aliyu HN, Majidadi RW, Oyeyemi AY. Examining the reliability and validity of a modified version of the International Physical Activity Questionnaire, long form (IPAQ-LF) in Nigeria: a cross-sectional study. BMJ Open. 2014;4(12):e005820–e005820. pmid:25448626
  45. 45. Segura-Jimenez V, Munguia-Izquierdo D, Camiletti-Moiron D, Alvarez-Gallardo IC, Ortega FB, Ruiz JR, et al. Comparison of the International Physical Activity Questionnaire (IPAQ) with a multi-sensor armband accelerometer in women with fibromyalgia: The al-Andalus project. Clin Exp Rheumatol. 2013;31(6 Suppl 79):2.
  46. 46. Dahl-Petersen IK, Hansen AW, Bjerregaard P, JøRgensen ME, Brage S. Validity of the International Physical Activity Questionnaire in the Arctic: Med Sci Sports Exerc. 2013;45(4):728–736. pmid:23190587
  47. 47. Brown WJ, Trost SG, Bauman A, Mummery K, Owen N. Test-retest reliability of four physical activity measures used in population surveys. J Sci Med Sport. 2004;7(2):205–215. pmid:15362316
  48. 48. Curry WB, Thompson JL. Comparability of accelerometer- and IPAQ-derived physical activity and sedentary time in South Asian women: A cross-sectional study. Eur J Sport Sci. 2014:1–8. http://dx.doi.org/10.1080/17461391.2014.957728.
  49. 49. Dyrstad SM, Hansen BH, Holme IM, Anderssen SA. Comparison of Self-reported versus Accelerometer-Measured Physical Activity: Med Sci Sports Exerc. 2014;46(1):99–106. pmid:23793232
  50. 50. Ekelund U, Sepp H, Brage S, Becker W, Jakes R, Hennings M, et al. Criterion-related validity of the last 7-day, short form of the International Physical Activity Questionnaire in Swedish adults. Public Health Nutr. 2006;9(2). http://dx.doi.org/10.1079/PHN2005840.
  51. 51. Kolbe-Alexander TL, Lambert EV, Harkins JB, Ekelund U. Comparison of two methods of measuring physical activity in South African older adults. J Aging Phys Act. 2006;14(1):98. pmid:16648654
  52. 52. Kurtze N, Rangul V, Hustvedt B-E. Reliability and validity of the international physical activity questionnaire in the Nord-Trondelag health study (HUNT) population of men. BMC Med Res Methodol. 2008;8(1):63. http://dx.doi.org/10.1186/1471-2288-8-63.
  53. 53. Marmeleira J, Laranjo L, Marques O, Batalha N. Criterion related validity of the short form of the International Physical Activity Questionnaire in adults who are blind. J Vis Impair Blind. 2013;107(5):375–381.
  54. 54. Oyeyemi AL, Oyeyemi AY, Adegoke BO, Oyetoke FO, Aliyu HN, Aliyu SU, et al. The Short International Physical Activity Questionnaire: cross-cultural adaptation, validation and reliability of the Hausa language version in Nigeria. BMC Med Res Methodol. 2011;11(1):156.
  55. 55. Oyeyemi AL, Umar M, Oguche F, Aliyu SU, Oyeyemi AY. Accelerometer-Determined Physical Activity and Its Comparison with the International Physical Activity Questionnaire in a Sample of Nigerian Adults. PLoS ONE. 2014;9(1):e87233. pmid:24489876
  56. 56. Papathanasiou G, Georgoudis G, Papandreou M, Spyropoulos P, Georgakopoulos D, Kalfakakou V, et al. Reliability measures of the short International Physical Activity Questionnaire (IPAQ) in Greek young adults. Hellenic J Cardiol. 2009;50(4):283–294. pmid:19622498
  57. 57. Wang C, Chen P, Zhuang J. Validity and Reliability of International Physical Activity Questionnaire–Short Form in Chinese Youth. Res Q Exerc Sport. 2013;84(sup2):S80–S86. http://dx.doi.org/10.1080/02701367.2013.850991.
  58. 58. Visser M, Koster A. Development of a questionnaire to assess sedentary time in older persons—a comparative study using accelerometry. BMC Geriatr. 2013;13:80. pmid:23899190
  59. 59. Johansen KL, Painter P, Delgado C, Doyle J. Characterization of Physical Activity and Sitting Time Among Patients on Hemodialysis Using a New Physical Activity Instrument. J Ren Nutr. 2015;25(1):25–30. pmid:25213326
  60. 60. Lagersted-Olsen J, Korshøj M, Skotte J, Carneiro I, Søgaard K, Holtermann A. Comparison of Objectively Measured and Self-reported Time Spent Sitting. Int J Sports Med. 2013;35(6):534–540. pmid:24258469
  61. 61. Marshall AL, Miller YD, Burton NW, Brown WJ. Measuring Total and Domain-Specific Sitting A Study of Reliability and Validity. Med Sci Sports Exerc. 2010;42(6):1094–1102. pmid:19997030
  62. 62. Chau JY, Van Der Ploeg HP, Dunn S, Kurko J, Bauman AE. Validity of the Occupational Sitting and Physical Activity Questionnaire. Med Sci Sports Exer. 2012;44(1):118–125. http://dx.doi.org/10.1249/MSS.0b013e3182251060.
  63. 63. Anjana RM, Sudha V, Lakshmipriya N, Subhashini S, Pradeepa R, Geetha L, et al. Reliability and validity of a new physical activity questionnaire for India. Int J Behav Nutr Phys Act. 2015;12(1):40. http://dx.doi.org/10.1186/s12966-015-0196-2.
  64. 64. Whitfield GP, Pettee Gabriel KK, Kohl HW. Assessing Sitting Across Contexts: Development of the Multi-Context Sitting Time Questionnaire. Res Q Exerc Sport. 2013;84(3):323–328. pmid:24261011
  65. 65. Jancey J, Tye M, McGann S, Blackford K, Lee AH. Application of the Occupational Sitting and Physical Activity Questionnaire (OSPAQ) to office based workers. BMC Public Health. 2014;14:762. pmid:25069528
  66. 66. James F. Sallis PKS. Validation of interviewer- and self-administered physical activity checklists for fifth grade students. Med Sci Sports Exer. 1996;28(7):840–851. http://dx.doi.org/10.1097/00005768-199607000-00011.
  67. 67. Adami F, Bergamaschi DP, Hinnig P de F, Oliveira NS. Validity study of the “Physical Activity Checklist” in children. Revista de Saúde Pública. 2013;47(3):488–496. http://dx.doi.org/10.1590/S0034-8910.2013047004018. pmid:24346561
  68. 68. Adami F, Cruciani F, Douek M, Sewell CD, Mariath AB, Hinnig PD, et al. Reliability of the Brazilian version of the Physical Activity Checklist Interview in children. Revista de Saúde Pública. 2011;45(2):321–333. http://dx.doi.org/10.1590/S0034-89102011000200011. pmid:21412572
  69. 69. Simpson K, Parker B, Capizzi J, Thompson P, Clarkson P, Freedson P, et al. Validity and reliability question 8 of the Paffenbarger Physical Activity Questionnaire among healthy adults. J Phys Act Health. 2015;12(1):116–123. pmid:24733349
  70. 70. Buchowski MS, Matthews CE, Cohen SS, Signorello LB, Fowke JH, Hargreaves MK, et al. Evaluation of a questionnaire to assess sedentary and active behaviors in the southern community cohort study. J Phy Act Health. 2012;9(6):765.
  71. 71. Scholes S, Coombs N, Pedisic Z, Mindell JS, Bauman A, Rowlands AV, et al. Age- and Sex-Specific Criterion Validity of the Health Survey for England Physical Activity and Sedentary Behavior Assessment Questionnaire as Compared With Accelerometry. Am J Epidemiol. 2014;179(12):1493–1502. pmid:24863551
  72. 72. Clark BK, Winkler E, Healy GN, Gardiner PG, Dunstan DW, Owen N, et al. Adults’ past-day recall of sedentary time: reliability, validity, and responsiveness. Med Sci Sports Exerc. 2013;45(6):1198–1207. pmid:23274615
  73. 73. Clark BK, Pavey TG, Lim RF, Gomersall SR, Brown WJ. Past-day recall of sedentary time: Validity of a self-reported measure of sedentary time in a university population. J Sci Med Sport. 2016;19(3):237–241. pmid:25766507
  74. 74. Ota E, Haruna M, Yanai H, Suzuki M, Anh DD, Matsuzaki M, et al. Reliability and validity of the Vietnamese version of the Pregnancy Physical Activity Questionnaire (PPAQ). Southeast Asian J Trop Med Public Health. 2008;39(3):562–70. pmid:18564699
  75. 75. Tessier S, Vuillemin A, Briançon S. Propriétés psychométriques d’un questionnaire de mesure de l’activité physique chez l’enfant scolarisé âgé de six à dix ans: QAPE-semaine. Sci & Sports. 2007;22(5):224–231. http://dx.doi.org/10.1016/j.scispo.2007.07.002.
  76. 76. Shuval K, Kohl HW, Bernstein I, Cheng D, Gabriel KP, Barlow CE, et al. Sedentary behaviour and physical inactivity assessment in primary care: the Rapid Assessment Disuse Index (RADI) study. Br J Sports Med. 2014;48(3):250–255. pmid:24144532
  77. 77. Golubic R, May AM, Borch KB, Overvad K, Charles MA, Diaz MJ, et al. Validity of electronically administered Recent Physical Activity Questionnaire (RPAQ) in ten European countries. PLoS ONE. 2014;9(3):e92829. pmid:24667343
  78. 78. Besson H, Brage S, Jakes RW, Ekelund U, Wareham NJ. Estimating physical activity energy expenditure, sedentary time, and physical activity intensity by self-report in adults. Am J Clin Nutr. 2010;91(1):106–114. pmid:19889820
  79. 79. Brown TD, Holland BV. Test-retest reliability of the self-assessed physical activity checklist. Percept Mot Skills. 2004;99(3 Pt 2):1099–1102. http://dx.doi.org/10.2466/pms.99.3f.1099-1102.
  80. 80. Dollman J, Pontt JL, Rowlands AV. Validity of self-reported sedentary time differs between Australian rural men engaged in office and farming occupations. J Sports Sci. 2016;34(12):1154–1158. pmid:26430922
  81. 81. Munguia-Izquierdo D, Segura-Jiménez V, Camiletti-Moirón D, et al. Spanish adaptation and psychometric properties of the Sedentary Behaviour Questionnaire for fibromyalgia patients: the al-Andalus study. Clin Exp Rheumatol. 2013;31(6 Suppl 79):S22–33.
  82. 82. Pedisic Z, Bennie JA, Timperio AF, Crawford DA, Dunstan DW, Bauman AE, et al. Workplace Sitting Breaks Questionnaire (SITBRQ): an assessment of concurrent validity and test-retest reliability. BMC Public Health. 2014;14(1):1249.
  83. 83. Lynch BM, Friedenreich CM, Khandwala F, Liu A, Nicholas J, Csizmadi I. Development and testing of a past year measure of sedentary behavior: the SIT-Q. BMC Public Health. 2014;14(1):899.
  84. 84. Wijndaele K, De Bourdeaudhuij I, Godino JG, Lynch BM, Griffin SJ, Westgate K, et al. Reliability and Validity of a Domain-Specific Last 7-d Sedentary Time Questionnaire. Med Sci Sports Exerc. 2014;46(6):1248–1260. pmid:24492633
  85. 85. Orsini N, Bellocco R, Bottai M, Pagano M, Wolk A. Reproducibility of the past year and historical self-administered total physical activity questionnaire among older women. Eur J Epidemiol. 2007;22(6):363–368. pmid:17333471
  86. 86. Pettee KK, Ham SA, Macera CA, Ainsworth BE. The reliability of a survey question on television viewing and associations with health risk factors in US adults. Obesity (Silver Spring). 2009;17(3):487–493. http://dx.doi.org/10.1038/oby.2008.554.
  87. 87. Csizmadi I, Neilson HK, Kopciuk KA, Khandwala F, Liu A, Friedenreich CM, et al. The Sedentary Time and Activity Reporting Questionnaire (STAR-Q): Reliability and Validity Against Doubly Labeled Water and 7-Day Activity Diaries. Am J Epidemiol. 2014;180(4):424–435. pmid:25038920
  88. 88. Neilson HK, Ullman R, Robson PJ, Friedenreich CM, Csizmadi I. Cognitive testing of the STAR-Q: insights in activity and sedentary time reporting. J Phys Act Health. 2013;10(3):379–389. pmid:22820674
  89. 89. Rey-Lopez JP, Ruiz JR, Ortega FB, Verloigne M, Vicente-Rodriguez G, Gracia-Marco L, et al. Reliability and validity of a screen time-based sedentary behaviour questionnaire for adolescents: The HELENA study. Eur J Public Health. 2012;22(3):373–377. pmid:21498560
  90. 90. Clark B, Thorp A, Winkler E, Gardiner PA, Healy GN, Owen N, et al. Validity of Self-Report Measures of Workplace Sitting Time and Breaks in Sitting Time. Med Sci Sports Exerc. 2011:1. http://dx.doi.org/10.1249/MSS.0b013e31821820a2.
  91. 91. Gardiner PA, Clark BK, Healy GN, Eakin EG, Winkler EAH, Owen N. Measuring older adults’ sedentary time: reliability, validity, and responsiveness. Med Sci Sports Exerc. 2011;43(11):2127–2133. pmid:21448077
  92. 92. Van Cauwenberg J, Van Holle V, De Bourdeaudhuij I, Owen N, Deforche B. Older adults’ reporting of specific sedentary behaviors: validity and reliability. BMC Public Health. 2014;14(1):734.
  93. 93. McCormack G, Giles-Corti B, Milligan R. The test-retest reliability of habitual incidental physical activity. Aust N Z J Public Health. 2003;27(4):428–433. pmid:14705307
  94. 94. Chau JY, van der Ploeg HP, Dunn S, Kurko J, Bauman AE. A tool for measuring workers’ sitting time by domain: the Workforce Sitting Questionnaire. Br J Sports Med. 2011;45(15):1216–1222. pmid:21947817
  95. 95. De Abajo S, Larriba R, Marquez S. Validity and reliability of the Yale Physical Activity Survey in Spanish elderly. J Sports Med Phys Fitness. 2001;41(4):479–485. pmid:11687767
  96. 96. Lindamer LA, McKibbin C, Norman GJ, Jordan L, Harrison K, Abeyesinhe S, et al. Assessment of physical activity in middle-aged and older adults with schizophrenia. Schizophr Res. 2008;104(1–3):294–301. pmid:18550338
  97. 97. Pearson N, Biddle SJ. Sedentary behavior and dietary intake in children, adolescents, and adults: a systematic review. Am J Prev Med. 2011;41(2),178–188. pmid:21767726
  98. 98. Boulos R, Vikre EK, Oppenheimer S, Chang H, Kanarek RB. ObesiTV: how television is influencing the obesity epidemic. Physiol Behav. 2012;107(1),146–153. pmid:22677722
  99. 99. Holt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. Loneliness and social isolation as risk factors for mortality a meta-analytic review. Perspect Psychol Sci. 2015;10(2),227–237. pmid:25910392
  100. 100. Sallis JF, Owen N, & Fotheringham MJ. Behavioral epidemiology: a systematic framework to classify phases of research on health promotion and disease prevention. Ann Behav Med. 2000;22(4), 294–298. pmid:11253440
  101. 101. Valkenburg PM, Peter J, & Schouten AP. Friend networking sites and their relationship to adolescents' well-being and social self-esteem. CyberPsycholBehav. 2006;9(5):584–590.
  102. 102. Offer S, & Schneider B. Revisiting the gender gap in time-use patterns multitasking and well-being among mothers and fathers in dual-earner families. ASR. 2011;76(6):809–833. http://dx.doi.org/10.1177/0003122411425170.
  103. 103. Becker MW, Alzahabi R, & Hopwood CJ. Media multitasking is associated with symptoms of depression and social anxiety. Cyberpsychol Behav Soc Netw. 2013;16(2):132–135. pmid:23126438
  104. 104. Ainsworth BE, Haskell WL, Herrmann SD, et al. 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc. 2011;43(8):1575–81. pmid:21681120
  105. 105. Ainsworth BE, Caspersen CJ, Matthews CE, Mâsse LC, Baranowski T, Zhu W. Recommendations to improve the accuracy of estimates of physical activity derived from self report. J Phys Act Health. 2012;9(0 1):S76.
  106. 106. Chang L, Krosnick JA. Comparing Oral Interviewing with Self-Administered Computerized QuestionnairesAn Experiment. Public Opin Q. 2010. http://dx.doi.org/10.1093/poq/nfp090.