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List of Appendices

Exposure to Light at night (LAN) and risk of obesity: a systematic review and meta-analysis of

observational studies

Ka Yan Lai, Chinmoy Sarkar, Michael Y. Ni, John Gallacher, Chris Webster

Table of contentsAppendix A. PECOS statement 2Appendix B. PRISMA-P Checklist 3Appendix C. Search terms applied in search engines 5Appendix D. List of literature and websites for the construction of search terms

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Appendix E. List of literature for the use of search engines 9Appendix F. Data items 10Appendix G. Interpretation of I2 11Appendix H. Guidelines for assessment of risk of bias of individual studies (cohort and cross-sectional studies)

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Appendix I. Presentations of assessment of risk of bias 20Appendix J. Guidelines for assessment of confidence in the body of evidence 21Appendix K. Sensitivity analysis 23Appendix L. Summary for the risk of bias assessments 24Appendix M. Summary for assessments of risk of bias of individual studies – Cohort (longitudinal) studies

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Appendix N. Summary for assessments of risk of bias of individual studies – Cross-sectional studies

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Appendix O. Summary for the assessments of confidence in the body of evidence

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Appendix P. Funnel plots for assessments of publication bias 38References for the supplementary materials 40

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Appendix A. PECOS statement (Population-Exposure-Comparator-Outcome-Study Design (PECOS) statement)

The PECOS statement of the present review: ‘among the general population, what is the effect of the highest LAN exposure compared to the lowest LAN exposure on risk of obesity as evident from observational studies’

Table A1. PECOS statement for the review of the association between LAN exposure and risk of obesity.People Exposure Comparator Outcome Study designGeneral population, regardless of sex, age or similar.

Highest LAN exposure, both subjective or objective LAN measures were included.

Lowest LAN exposure, both subjective or objective LAN measures were included.

Primary outcome: obesity. No secondary outcome was included.

Observational studies, including both longitudinal and cross-sectional studies.

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Appendix B. PRISMA-P Checklist

Table B1. PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review protocol*

Section and topic Item No

Checklist item Reported on page

ADMINISTRATIVE INFORMATIONTitle:

Identification 1a Identify the report as a protocol of a systematic review P.1 Update 1b If the protocol is for an update of a previous systematic review, identify as such N/A

Registration 2 If registered, provide the name of the registry (such as PROSPERO) and registration number P.4Authors:

Contact 3a Provide name, institutional affiliation, e-mail address of all protocol authors; provide physical mailing address of corresponding author

Title page at the reviewing stage

Contributions 3b Describe contributions of protocol authors and identify the guarantor of the review P.5Amendments 4 If the protocol represents an amendment of a previously completed or published protocol, identify as such and list

changes; otherwise, state plan for documenting important protocol amendmentsSupport:

Sources 5a Indicate sources of financial or other support for the review N/A Sponsor 5b Provide name for the review funder and/or sponsor N/A Role of sponsor or funder

5c Describe roles of funder(s), sponsor(s), and/or institution(s), if any, in developing the protocol N/A

INTRODUCTIONRationale 6 Describe the rationale for the review in the context of what is already known P. 3-4Objectives 7 Provide an explicit statement of the question(s) the review will address with reference to participants, interventions,

comparators, and outcomes (PICO)P. 4

METHODSEligibility criteria 8 Specify the study characteristics (such as PICO, study design, setting, time frame) and report characteristics (such as

years considered, language, publication status) to be used as criteria for eligibility for the reviewP. 5

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Information sources 9 Describe all intended information sources (such as electronic databases, contact with study authors, trial registers or other grey literature sources) with planned dates of coverage

P. 4-5

Search strategy 10 Present draft of search strategy to be used for at least one electronic database, including planned limits, such that it could be repeated

P. 4-5

Study records: Data management

11a Describe the mechanism(s) that will be used to manage records and data throughout the review P.6-8

Selection process

11b State the process that will be used for selecting studies (such as two independent reviewers) through each phase of the review (that is, screening, eligibility and inclusion in meta-analysis)

P.5 & Fig. 1

Data collection process

11c Describe planned method of extracting data from reports (such as piloting forms, done independently, in duplicate), any processes for obtaining and confirming data from investigators

P.5

Data items 12 List and define all variables for which data will be sought (such as PICO items, funding sources), any pre-planned data assumptions and simplifications

P.6-7 & Tables 1-2

Outcomes and prioritization

13 List and define all outcomes for which data will be sought, including prioritization of main and additional outcomes, with rationale

P.5

Risk of bias in individual studies

14 Describe anticipated methods for assessing risk of bias of individual studies, including whether this will be done at the outcome or study level, or both; state how this information will be used in data synthesis

P.8

Data synthesis 15a Describe criteria under which study data will be quantitatively synthesised P.6-815b If data are appropriate for quantitative synthesis, describe planned summary measures, methods of handling data and

methods of combining data from studies, including any planned exploration of consistency (such as I2, Kendall’s τ)P.6-8

15c Describe any proposed additional analyses (such as sensitivity or subgroup analyses, meta-regression) P.6-815d If quantitative synthesis is not appropriate, describe the type of summary planned N/A

Meta-bias(es) 16 Specify any planned assessment of meta-bias(es) (such as publication bias across studies, selective reporting within studies)

P.8

Confidence in cumulative evidence

17 Describe how the strength of the body of evidence will be assessed (such as GRADE) P.5-6

* It is strongly recommended that this checklist be read in conjunction with the PRISMA-P Explanation and Elaboration (cite when available) for important clarification on the items. Amendments to a review protocol should be tracked and dated. The copyright for PRISMA-P (including checklist) is held by the PRISMA-P Group and is distributed under a Creative Commons Attribution Licence 4.0.

From: Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart L, PRISMA-P Group. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015 Jan 2;349(jan02 1):g764

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Appendix C. Search terms applied in search engines

The following two tables showed the search terms being applied in search engines.

Table C1. Search strategy 1Title/abstract

AND

Title/abstractLight-related search terms Body weight-related search terms

light exposure OR artificial light at night OR artificial light-at-night OR night time light

OR nighttime light OR night light OR bedroom light OR light at night OR

environmental lighting OR ambient light OR dim light at night OR light pollution OR

domestic light

adiposity OR body mass index OR bmi OR body weight OR weight OR obesity

OR overweight OR over-weight OR diabetes OR type 2 diabetes

Table C2. Search strategy 2Title/abstract

AND

Title/abstract

AND

Title/abstractLight-emitted device

use-search termsBody weight-related

search termsNighttime use- search terms

electronic OR technology OR television OR media

OR phone

adiposity OR body mass index OR bmi OR body weight OR weight OR

obesity OR overweight OR over-weight OR diabetes

OR type 2 diabetes

night-time OR sleep OR bedroom OR

night

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The following two tables demonstrate the applications of the first search strategy into the search engines. Following the search as demonstrated below, we applied filters limiting the search in human.

Table C3. Search terms (search strategy 1) applied in MEDLINE (EBSCO), Academic Search Complete (EBSCO), CINAHL Plus (EBSCO) in search for evidence of the association between LAN exposure and risk of obesity.Search ID Search termsS1 light exposure OR artificial light at night OR artificial light-at-night OR night

time light OR nighttime light OR night light OR bedroom light OR light at night OR environmental lighting OR ambient light OR dim light at night OR light pollution

S2 domestic lightS3 adiposity OR body mass index OR bmi OR body weight OR weight OR obesity

OR overweight OR over-weight OR diabetes OR type 2 diabetesS4 S1 OR S2 S5 TI S4 AND TI S3 S6 AB S4 AND AB S3 S7 S5 OR S6

Note. The maximum number of fields available for each search was 12. A ‘Limiter – Human’ was applied following the search.

Table C4. Search terms (search strategy 1) applied in PubMed in search for evidence of the association between LAN exposure and risk obesity.Search ID Search terms#1 (((((((((((light exposure[Title/Abstract]) OR artificial light at

night[Title/Abstract]) OR artificial light-at-night[Title/Abstract]) OR night time light[Title/Abstract]) OR nighttime light[Title/Abstract]) OR night light[Title/Abstract]) OR bedroom light[Title/Abstract]) OR light at night[Title/Abstract]) OR environmental lighting[Title/Abstract]) OR ambient light[Title/Abstract]) OR dim light at night[Title/Abstract]) OR light pollution[Title/Abstract]

#2 (((((((((adiposity[Title/Abstract]) OR body mass index[Title/Abstract]) OR bmi[Title/Abstract]) OR body weight[Title/Abstract]) OR weight[Title/Abstract]) OR obesity[Title/Abstract]) OR overweight[Title/Abstract]) OR over-weight[Title/Abstract]) OR diabetes[Title/Abstract]) OR type 2 diabetes[Title/Abstract]

#3 (#1) AND #2 #4 (#1) AND #2 Filters: Humans

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The following two tables demonstrate the applications of the second search strategy into the search engines. Following the search as demonstrated below, we applied filters limiting the search in human.

Table C5. Search terms (search strategy 2) applied in MEDLINE (EBSCO), Academic Search Complete (EBSCO), CINAHL Plus (EBSCO) in search for evidence of the association between LAN exposure and risk of obesity.Search ID Search termsS1 electronic OR technology OR television OR media OR phoneS2 adiposity OR body mass index OR bmi OR body weight OR weight OR obesity

OR overweight OR over-weight OR diabetes OR type 2 diabetesS3 night-time OR sleep OR bedroom OR nightS4 TI S1 AND TI S2 AND TI S3 S5 AB S1 AND AB S2 AND AB S3 S6 S4 OR S5

Note. The maximum number of fields available for each search was 12. A ‘Limiter – Human’ was applied following the search.

Table C6. Search terms (search strategy 2) applied in PubMed in search for evidence of the association between LAN exposure and risk obesity.Search ID Search terms#1 ((((electronic[Title/Abstract]) OR technology[Title/Abstract]) OR

television[Title/Abstract]) OR media[Title/Abstract]) OR phone[Title/Abstract]#2 (((((((((adiposity[Title/Abstract]) OR body mass index[Title/Abstract]) OR

bmi[Title/Abstract]) OR body weight[Title/Abstract]) OR weight[Title/Abstract]) OR obesity[Title/Abstract]) OR overweight[Title/Abstract]) OR over-weight[Title/Abstract]) OR diabetes[Title/Abstract]) OR type 2 diabetes[Title/Abstract]

#3 (((night-time[Title/Abstract]) OR sleep[Title/Abstract]) OR bedroom[Title/Abstract]) OR night[Title/Abstract]

#4 ((#1) AND #2) AND #3#5 ((#1) AND #2) AND #3 Filters: Humans

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Appendix D. List of literature and websites for the construction of search terms

A. Journal articles

1. Butt, M., 2012. Estimation of Light Pollution Using Satellite Remote Sensing and Geographic Information System Techniques. GIScience & Remote Sensing 49, 609-621. https://doi.org/10.2747/1548-1603.49.4.609

2. Cajochen, C., Frey, S., Anders, D., Späti, J., Bues, M., Pross, A., Mager, R., Wirz-Justice, A., Stefani, O., 2011. Evening exposure to a light-emitting diodes (LED)-backlit computer screen affects circadian physiology and cognitive performance. Journal of applied physiology (Bethesda, Md. : 1985) 110, 1432-1438. https://doi.org/10.1152/japplphysiol.00165.2011

3. Cho, Y., Ryu, S.-H., Lee, B.R., Kim, K.H., Lee, E., Choi, J., 2015. Effects of artificial light at night on human health: A literature review of observational and experimental studies applied to exposure assessment. Chronobiol. Int. 32, 1294-1310. https://doi.org/10.3109/07420528.2015.1073158

4. Salgado-Delgado, R., Tapia Osorio, A., Saderi, N., Escobar, C., 2011. Disruption of Circadian Rhythms: A Crucial Factor in the Etiology of Depression. Depression Research and Treatment 2011. https://doi.org/10.1155/2011/839743

5. Stevens, R., Rea, M., 2001. Light in the Built Environment: Potential role of Circadian Disruption in Endocrine Disruption and Breast Cancer. Cancer Causes Control 12, 279-287. https://doi.org/10.1023/A:1011237000609

6. Touitou, Y., Reinberg, A., Touitou, D., 2017. Association between light at night, melatonin secretion, sleep deprivation, and the internal clock: Health impacts and mechanisms of circadian disruption. Life Sci. 173, 94. https://doi.org/10.1016/j.lfs.2017.02.008

B. Websites

1. Environment Bureau, The Government of the Hong Kong Special Administrative Region, 2019. Review on measures managing external lighting and future developments, Hong Kong. http://www.charteronexternallighting.gov.hk/en/index.html (accessed 28 April 2019)

2. Rensselaer Polytechnic Institute, 2019. The Light and Health Alliance, US. https://www.lrc.rpi.edu/programs/lightHealth/alliance.asp (accessed 28 April 2019)

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https://www.lrc.rpi.edu/programs/lightHealth/alliance.asp

http://www.charteronexternallighting.gov.hk/en/index.html

https://doi.org/10.1023/A:1011237000609

https://doi.org/10.1155/2011/839743

https://doi.org/10.3109/07420528.2015.1073158

https://doi.org/10.1152/japplphysiol.00165.2011

https://doi.org/10.2747/1548-1603.49.4.609

Appendix E. List of literature for the use of search engines

A. Journal article – articles analyzing the characterstics of various search engines

1. Kelly, L., St Pierre-Hansen, N., 2008. So many databases, such little clarity: Searching the literature for the topic aboriginal. Canadian family physician Medecin de famille canadien 54, 1572-1573.

2. McKibbon, K.A., Marks, S., 1998. Searching for the best evidence. Part 2: searching CINAHL and Medline. Evidence Based Nursing 1, 105. https://doi.org/10.1136/ebn.1.4.105

3. Williamson, P.O., Minter, C.I.J., 2019. Exploring PubMed as a reliable resource for scholarly communications services. Journal of the Medical Library Association : JMLA 107, 16. https://doi.org/10.5195/jmla.2019.433

B. Journal article – prior relevant review articles

1. Roberts, H., Van Lissa, C., Hagedoorn, P., Kellar, I., Helbich, M., 2019. The effect of short-term exposure to the natural environment on depressive mood: A systematic review and meta-analysis. Environ. Res. 177, urn:issn:0013-9351.

2. Yang, B.Y., Fan, S., Thiering, E., Seissler, J., Nowak, D., Dong, G.H., Heinrich, J., 2020. Ambient air pollution and diabetes: A systematic review and meta-analysis. Environ. Res. 180. https://doi.org/10.1016/j.envres.2019.108817

3. Zare Sakhvidi, M.J., Zare Sakhvidi, F., Mehrparvar, A.H., Foraster, M., Dadvand, P., 2018. Association between noise exposure and diabetes: A systematic review and meta-analysis. Environ. Res. 166, 647-657. https://doi.org/10.1016/j.envres.2018.05.011

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https://doi.org/10.1016/j.envres.2018.05.011

https://doi.org/10.1016/j.envres.2019.108817

https://doi.org/10.5195/jmla.2019.433

https://doi.org/10.1136/ebn.1.4.105

Appendix F. Data items

Table F1. Table for data items being extracted from each included study.

Data itemsAuthorYearFunding sourceConflict of interestStudy populationDates of studyGeographical settingAgeSexOccupationNumber of participantsRecruitment strategyFollow-up duration (for longitudinal study)Response rateInclusion/exclusion criteriaStudy designDefinition of obesityMethod of diagnosis Definition of light at nightConfounders or modifying factorsMain statistical method

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Appendix G. Interpretation of I2

As shown in Higgins et al. (2003), ‘we would tentatively assign adjectives of low, moderate, and high to I2 values of 25%, 50%, and 75%’ (p. 559). Also specified in Higgins and Green (2011), the heterogeneity across studies can be interpreted as follows:

Table G1. Interpretation of I2

I2 Level of heterogeneity0% to 40% might not be important30% to 60% may represent moderate heterogeneity*50% to 90% may represent substantial heterogeneity*75% to 100% considerable heterogeneity*

*The importance of the observed value of I2 depends on (i) magnitude and direction of effects and (ii) strength of evidence for heterogeneity (e.g. P value from the chi-squared test, or a confidence interval for I2).

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Appendix H. Guidelines for assessment of risk of bias of individual studies (cohort and cross-sectional studies)

The (Office of Health Assessment and Translation) OHAT risk of bias rating tool (Rooney et al., 2014) was adopted to assess the risk of bias of individual studies (National Toxicology Program, 2015). Seven risk of bias domains ((1) selection bias, (2) confounding bias, (3) attrition/exclusion bias, (4) detection bias for exposure, (5) detection bias for outcome, (6) selective reporting bias and (7) other bias) with different criteria for cohort and cross-sectional studies were applied. All the risk of bias domains were rated as ‘definitely Low’, ‘probably low’, ‘probably high’ or ‘definitely high’. A rate of ‘not reported’ was assigned when inadequate information was provided. The criteria for assessing was also taken reference from Johnson et al. (2014).

1. Selection bias - Did selection of study participants result in appropriate comparison groups?

Definitely Low risk of bias:

Cohort & Cross-sectional: There is direct evidence that subjects (both exposed and non-exposed) were similar (e.g., recruited from the same eligible population, recruited with the same method of ascertainment using the same inclusion and exclusion criteria, and were of similar age and health status), recruited within the same time frame, and had the similar participation/response rates.

Probably Low risk of bias:

Cohort & Cross-sectional: There is indirect evidence that subjects (both exposed and non-exposed) were similar (e.g., recruited from the same eligible population, recruited with the same method of ascertainment using the same inclusion and exclusion criteria, and were of similar age and health status), recruited within the same time frame, and had the similar participation/response rates, OR differences between groups would not appreciably bias results.

Probably High risk of bias:

Cohort & Cross-sectional: There is indirect evidence that subjects (both exposed and non-exposed) were not similar, recruited within very different time frames, or had the very different participation/response rates, OR there is insufficient information provided about the comparison group including a different rate of non-response without an explanation (record “NR” as basis for answer).

Definitely High risk of bias:

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Cohort & Cross-sectional: There is direct evidence that subjects (both exposed and non-exposed) were not similar, recruited within very different time frames, or had the very different participation/response rates.

2. Confounding Bias - Did the study design or analysis account for important confounding and modifying variables?


Cohort & Cross-sectional: There is direct evidence that appropriate adjustments or explicit considerations were made for primary covariates and confounders in the final analyses through the use of statistical models to reduce research-specific bias including standardization, matching, adjustment in multivariate model, stratification, propensity scoring, or other methods that were appropriately justified. Acceptable consideration of appropriate adjustment factors includes cases when the factor is not included in the final adjustment model because the author conducted analyses that indicated it did not need to be included, AND there is direct evidence that primary covariates and confounders were assessed using valid and reliable measurements, AND there is direct evidence that other exposures anticipated to bias results were not present or were appropriately measured and adjusted for. In occupational studies or studies of contaminated sites, other chemical exposures known to be associated with those settings were appropriately considered.


Cohort & Case control: There is indirect evidence that appropriate adjustments were made, OR it is deemed that not considering or only considering a partial list of covariates or confounders in the final analyses would not appreciably bias results. AND there is evidence (direct or indirect) that primary covariates and confounders were assessed using valid and reliable measurements, OR it is deemed that the measures used would not appreciably bias results (i.e., the authors justified the validity of the measures from previously published research), AND there is evidence (direct or indirect) that other co-exposures anticipated to bias results were not present or were appropriately adjusted for, OR it is deemed that co-exposures present would not appreciably bias results.

Note: As discussed above, this includes insufficient information provided on co-exposures in general population studies.


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Cohort & Cross-sectional: There is indirect evidence that the distribution of primary covariates and known confounders differed between the groups and was not appropriately adjusted for in the final analyses, OR there is insufficient information provided about the distribution of known confounders (record “NR” as basis for answer), OR there is indirect evidence that primary covariates and confounders were assessed using measurements of unknown validity, OR there is insufficient information provided about the measurement techniques used to assess primary covariates and confounders (record “NR” as basis for answer), OR there is indirect evidence that there was an unbalanced provision of additional co-exposures across the primary study groups, which were not appropriately adjusted for, OR there is insufficient information provided about co-exposures in occupational studies or studies of contaminated sites where high exposures to other chemical exposures would have been reasonably anticipated (record “NR” as basis for answer).


Cohort & Cross-sectional: There is direct evidence that the distribution of primary covariates and known confounders differed between the groups, confounding was demonstrated, and was not appropriately adjusted for in the final analyses, OR there is direct evidence that primary covariates and confounders were assessed using non valid measurements, OR there is direct evidence that there was an unbalanced provision of additional co-exposures across the primary study groups, which were not appropriately adjusted for.

3. Attrition/Exclusion Bias - Were outcome data complete without attrition or exclusion from analysis?


Cohort: There is direct evidence that loss of subjects (i.e., incomplete outcome data) was adequately addressed and reasons were documented when human subjects were removed from a study. Acceptable handling of subject attrition includes: very little missing outcome data; reasons for missing subjects unlikely to be related to outcome (for survival data, censoring unlikely to be introducing bias); missing outcome data balanced in numbers across study groups, with similar reasons for missing data across groups, OR missing data have been imputed using appropriate methods and characteristics of subjects lost to follow up or with unavailable records are described in identical way and are not significantly different from those of the study participants.

Cross-sectional: There is direct evidence that exclusion of subjects from analyses was adequately addressed, and reasons were documented when subjects were removed from the study or excluded from analyses.

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Cohort: There is indirect evidence that loss of subjects (i.e., incomplete outcome data) was adequately addressed and reasons were documented when human subjects were removed from a study, OR it is deemed that the proportion lost to follow-up would not appreciably bias results. This would include reports of no statistical differences in characteristics of subjects lost to follow up or with unavailable records from those of the study participants. Generally, the higher the ratio of participants with missing data to participants with events, the greater potential there is for bias. For studies with a long duration of follow-up, some withdrawals for such reasons are inevitable.

Cross-sectional: There is indirect evidence that exclusion of subjects from analyses was adequately addressed, and reasons were documented when subjects were removed from the study or excluded from analyses.


Cohort: There is indirect evidence that loss of subjects (i.e., incomplete outcome data) was unacceptably large and not adequately addressed, OR there is insufficient information provided about numbers of subjects lost to follow-up (record “NR” as basis for answer).

Cross-sectional: There is indirect evidence that exclusion of subjects from analyses was not adequately addressed, OR there is insufficient information provided about why subjects were removed from the study or excluded from analyses (record “NR” as basis for answer).


Cohort: There is direct evidence that loss of subjects (i.e., incomplete outcome data) was unacceptably large and not adequately addressed. Unacceptable handling of subject attrition includes: reason for missing outcome data likely to be related to true outcome, with either imbalance in numbers or reasons for missing data across study groups; or potentially inappropriate application of imputation.

Cross-sectional: There is direct evidence that exclusion of subjects from analyses was not adequately addressed. Unacceptable handling of subject exclusion from analyses includes: reason for exclusion likely to be related to true outcome, with either imbalance in numbers or reasons for exclusion across study groups.

4. Detection Bias - Can we be confident in the exposure characterization?


Cohort & Cross-sectional: There is direct evidence that exposure was consistently assessed (i.e., under the same method and time-frame) using well-established methods that directly measure

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exposure (e.g., measurement of the chemical in air or measurement of the chemical in blood, plasma, urine, etc.), OR exposure was assessed using less-established methods that directly measure exposure and are validated against well-established methods.


Cohort & Cross-sectional: There is indirect evidence that the exposure was consistently assessed using well-established methods that directly measure exposure, OR exposure was assessed using indirect measures (e.g., questionnaire or occupational exposure assessment by a certified industrial hygienist) that have been validated or empirically shown to be consistent with methods that directly measure exposure (i.e., inter-methods validation: one method vs. another).


Cohort & Cross-sectional: There is indirect evidence that the exposure was assessed using poorly validated methods that directly measure exposure, OR there is direct evidence that the exposure was assessed using indirect measures that have not been validated or empirically shown to be consistent with methods that directly measure exposure (e.g., a job-exposure matrix or self-report without validation) (record “NR” as basis for answer), OR there is insufficient information provided about the exposure assessment, including validity and reliability, but no evidence for concern about the method used (record “NR” as basis for answer).


Cohort & Cross-sectional: There is direct evidence that the exposure was assessed using methods with poor validity, OR evidence of exposure misclassification (e.g., differential recall of self-reported exposure).

5. Detection Bias - Can we be confident in the outcome assessment?


Cohort: There is direct evidence that the outcome was assessed using well-established methods (e.g., the “gold standard” with validity and reliability >0.70 Genaidy et al. 2007), AND subjects had been followed for the same length of time in all study groups. Acceptable assessment methods will depend on the outcome, but examples of such methods may include: objectively measured with diagnostic methods, measured by trained interviewers, obtained from registries (Shamliyan et al. 2010), AND there is direct evidence that the outcome assessors (including study subjects, if outcomes were self-reported) were adequately blinded to the study group, and it is unlikely that they could have broken the blinding prior to reporting outcomes.

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Cross-sectional: There is direct evidence that the outcome was assessed using well-established methods (the gold standard), AND there is direct evidence that the outcome assessors (including study subjects, if outcomes were self-reported) were adequately blinded to the exposure level, and it is unlikely that they could have broken the blinding prior to reporting outcomes.


Cohort: There is indirect evidence that the outcome was assessed using acceptable methods (i.e., deemed valid and reliable but not the gold standard) (e.g., validity and reliability ≥0.40 Genaidy et al. 2007), AND subjects had been followed for the same length of time in all study groups [Acceptable, but not ideal assessment methods will depend on the outcome, but examples of such methods may include proxy reporting of outcomes and mining of data collected for other purposes], OR it is deemed that the outcome assessment methods used would not appreciably bias results, AND there is indirect evidence that the outcome assessors (including study subjects, if outcomes were self-reported) were adequately blinded to the study group, and it is unlikely that they could have broken the blinding prior to reporting outcomes, OR it is deemed that lack of adequate blinding of outcome assessors would not appreciably bias results, which is more likely to apply to objective outcome measures.

Cross-sectional: There is indirect evidence that the outcome was assessed using acceptable methods, OR it is deemed that the outcome assessment methods used would not appreciably bias results, AND there is indirect evidence that the outcome assessors were adequately blinded to the exposure level, and it is unlikely that they could have broken the blinding prior to reporting outcomes, OR it is deemed that lack of adequate blinding of outcome assessors would not appreciably bias results (including that subjects self-reporting outcomes were likely not aware of reported links between the exposure and outcome lack of blinding is unlikely to bias a particular outcome).


Cohort: There is indirect evidence that the outcome assessment method is an insensitive instrument (e.g., a questionnaire used to assess outcomes with no information on validation), OR the length of follow up differed by study group, OR there is indirect evidence that it was possible for outcome assessors (including study subjects if outcomes were self-reported) to infer the study group prior to reporting outcomes, OR there is insufficient information provided about blinding of outcome assessors (record “NR” as basis for answer).

Cross-sectional: There is indirect evidence that the outcome assessment method is an insensitive instrument,

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OR there is indirect evidence that it was possible for outcome assessors to infer the exposure level prior to reporting outcomes (including that subjects self-reporting outcomes were likely aware of reported links between the exposure and outcome), OR there is insufficient information provided about blinding of outcome assessors (record “NR” as basis for answer).


Cohort: There is direct evidence that the outcome assessment method is an insensitive instrument, OR the length of follow up differed by study group, OR there is direct evidence for lack of adequate blinding of outcome assessors (including study subjects if outcomes were self-reported), including no blinding or incomplete blinding.

Cross-sectional: There is direct evidence that the outcome assessment method is an insensitive instrument, OR there is direct evidence that outcome assessors were aware of the exposure level prior to reporting outcomes (including that subjects self-reporting outcomes were aware of reported links between the exposure and outcome).

6. Selective Reporting Bias - Were all measured outcomes reported?


Cohort & Cross-sectional: There is direct evidence that all of the study’s measured outcomes (primary and secondary) outlined in the protocol, methods, abstract, and/or introduction (that are relevant for the evaluation) have been reported. This would include outcomes reported with sufficient detail to be included in meta-analysis or fully tabulated during data extraction and analyses had been planned in advance.


Cohort & Cross-sectional: There is indirect evidence that all of the study’s measured outcomes (primary and secondary) outlined in the protocol, methods, abstract, and/or introduction (that are relevant for the evaluation) have been reported, OR analyses that had not been planned in advance (i.e., retrospective unplanned subgroup analyses) are clearly indicated as such and it is deemed that the unplanned analyses were appropriate and selective reporting would not appreciably bias results (e.g., appropriate analyses of an unexpected effect). This would include outcomes reported with insufficient detail such as only reporting that results were statistically significant (or not).


Cohort & Cross-sectional: There is indirect evidence that all of the study’s measured outcomes (primary and secondary) outlined in the protocol, methods, abstract, and/or introduction (that are relevant for the evaluation) have been reported,

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OR and there is indirect evidence that unplanned analyses were included that may appreciably bias results, OR there is insufficient information provided about selective outcome reporting (record “NR” as basis for answer).


Cohort & Cross-sectional: There is direct evidence that all of the study’s measured outcomes (primary and secondary) outlined in the protocol, methods, abstract, and/or introduction (that are relevant for the evaluation) have not been reported. In addition to not reporting outcomes, this would include reporting outcomes based on composite score without individual outcome components or outcomes reported using measurements, analysis methods or subsets of the data (e.g., subscales) that were not pre-specified or reporting outcomes not pre-specified, or that unplanned analyses were included that would appreciably bias results.

7. Other Bias - Were there no other potential threats to internal validity (e.g., statistical methods were appropriate and researchers adhered to the study protocol)?

Criteria for a judgment of Definitely LOW risk of bias:

The study appears to be free of other sources of bias.

Criteria for the judgment of PROBABLY LOW risk of bias:

There is insufficient information to permit a judgment of definitely low risk of bias, but there is indirect evidence that suggests the study was free of other threats to validity.

Criteria for the judgment of PROBABLY HIGH risk of bias:

There is insufficient information to permit a judgment of definitely high risk of bias, but there is indirect evidence that suggests the study was not free of other threats to validity, as described by the criteria for a judgment of high risk of bias.

Criteria for the judgment of Definitely HIGH risk of bias: There is at least one important risk of bias. For example, the study:

Had a potential source of bias related to the specific study design used; or Stopped early due to some data-dependent process (including a formal-stopping

rule); or The conduct of the study is affected by interim results (e.g. recruiting additional

participants from a subgroup showing greater or lesser effect); or Has been claimed to have been fraudulent; or Had some other problem

19

Appendix I. Presentations of assessment of risk of bias

In order to visualize the severity of risk of bias across different studies, the answer format of the (Office of Health Assessment and Translation) OHAT risk of bias rating tool (Rooney et al., 2014) was adopted (National Toxicology Program, 2015).

Table I1. Answer format, definitions and explanations of assessment of risk of bias.

Answer format/symbol

Definition Explanations

Definitely Low risk of bias: There is direct evidence of low risk-of-bias practices (May include specific examples of relevant low risk-of-bias practices)

Probably Low risk of bias: There is indirect evidence of low risk-of-bias practices OR it is deemed that deviations from low risk-of-bias practices for these criteria during the study would not appreciably bias results, including consideration of direction and magnitude of bias.

Probably High risk of bias: There is indirect evidence of high risk-of-bias

practices OR there is insufficient information (e.g., not reported or “NR”) provided about relevant risk-of-bias practices

Definitely High risk of bias: There is direct evidence of high risk-of-bias practices (May include specific examples of relevant high risk-of-bias practices)

20

++

+

- NR

--

Appendix J. Guidelines for assessment of confidence in the body of evidence

The Grading of Recommendations Assessment, Development and Evaluation (GRADE) guideline (Guyatt et al., 2008) with a total of eight dimensions was employed to assess the confidence in the body of evidence. Five of the assessing dimensions could downgrade the overall quality of evidence, while three could upgrade the overall quality. The criteria for asessing the overall quality of evidence are summarized below based upon prior literatures (Guyatt et al., 2008; Guyatt et al., 2011a; Guyatt et al., 2011b; Guyatt et al., 2011c) and a relevant scoring system (Balshem et al., 2011).

Table J1. Guidelines of downgrading domains for assessing the overall quality of evidence.

Downgrading dimensions

Summary of criteria for downgrading Downgrade if

Risk of bias Downgrade if major limitations that may bias the estimates of the exposure effect exist (Guyatt et al., 2008). Examples of obvious risks of bias include:

- outcomes are subjective- assessment highly susceptible to bias- large lossess to follow-up

-1 serious-2 very serious

Indirectness Downgrade if the evidence in relation to population, exposure, comparator and outcome, is not comparable to the research question of interest (Guyatt et al., 2011a). Examples of obvious indirectness include:

- the population differs from those of interest- the exposure tested differs from the exposure

of interest- outcome differs from those of interest (e.g., the

use of surrogate)


Inconsistency Downgrade if differing estimates of the exposure effect across studies exist without plausible explanations (Guyatt et al., 2011b). Examples of differences include:

- the exposure has a higher effect among a specific group of population

- the exposure effect differs at different time points


Imprecision Downgrade if wide confidence intervals are identified stemming from studies with small sample size based on the judgement of the review panel (Guyatt et al., 2008).


Publication Bias Downgrade if the study investigators fail to report findings, especially those show statistical insignificance (Guyatt et al., 2008).

-1 likely-2 very likely

21

Table J2. Guidelines of upgrading domains for assessing the overall quality of evidence.

Upgrading dimensions

Summary of criteria for upgrading Upgrade if

Large magnitude of effect

Upgrade if methodological rigorous observational studies show significant reduction or increase in risk (Guyatt et al., 2011c). Examples of rating up include:

- Rating up one level for at least a two-fold reduction or increase in risk

- Rating up two levels for at least a five-fold reduction or increase in risk

+1 large+2 very large

Dose response Upgrade if a dose-response gradient exist in one or multiple studies (Guyatt et al., 2011c).

+1 evidence of a gradient

Confounding minimizes effect

Upgrade when all plausible confounders or biases would decrease an apparent treatment effect, or would create a spurious effect when results suggest no effect (Guyatt et al., 2011c). Examples of bias include:

- Different disease severity exists between patients in the two hospital, so bias results against a particular hospital with higher severity

+1 would suggest a spurious effect if no effect was observed

23

Appendix K. Sensitivity analysis

Table K1. Summary of sensitivity tests by weight-related outcomes.

Number of study

Summary odds ratio (95%CI)

Test of heterogeneityQ p-value I2 (%)

Overweight (BMI≥25 kg/m2)Excluded the highest estimate (1) 6 1.12 (1.09-1.16) 3.63 0.60 0Excluded the highest weight (2) 6 1.14 (1.10-1.19) 7.25 0.20 31.00Studies with male and female (3) 4 1.14 (1.04-1.26) 6.62 0.08 54.70Studies with female only (4) 3 1.13 (1.09-1.16) 1.57 0.46 0Aged ≥15 (5) 4 1.14 (1.09-1.19) 6.93 0.07 56.74Aged <15 (6) 2 1.07 (0.97-1.18) 0.10 0.75 0Cross-sectional studies only (7) 2 1.09 (0.97-1.22) 17.06 <0.0001 94.14Obesity (BMI≥30 kg/m2)Excluded the highest estimate (8) 4 1.16 (1.06-1.27) 20.74 0.0001 85.54Excluded the highest weight (9) 4 1.22 (1.16-1.28) 5.93 0.12 49.42Aged ≥15 (10) 2 1.13 (0.99-1.28) 16.67 <0.0001 94.00Aged <15 (11) 2 1.55 (1.18-2.04) 1.50 0.22 33.28Cross-sectional studies only (12) 5 1.19 (1.02-1.39) 47.49 <0.0001 91.58

(1) Park et al. (2019), Abay and Amare (2018), Dube et al. (2017), Koo et al. (2016), McFadden et al. (2014), Chahal et al. (2013).(2) Park et al. (2019), Abay and Amare (2018), Dube et al. (2017), Koo et al. (2016), Obayashi et al. (2013), Chahal et al. (2013).(3) Dube et al. (2017), Koo et al. (2016), Obayashi et al. (2013), Chahal et al. (2013).(4) Park et al. (2019), Abay and Amare (2018), McFadden et al. (2014).(5) Abay and Amare (2018), Koo et al. (2016), McFadden et al. (2014), Obayashi et al. (2013).(6) Dube et al. (2017), Chahal et al. (2013).(7) Park et al. (2019), Abay and Amare (2018).(8) Park et al. (2019), Abay and Amare (2018), McFadden et al. (2014), Chahal et al. (2013).(9) Park et al. (2019), Dube et al. (2017), McFadden et al. (2014), Chahal et al. (2013).(10) Abay and Amare (2018), McFadden et al. (2014).(11) Dube et al. (2017), Chahal et al. (2013).(12) Park et al. (2019), Abay and Amare (2018), Dube et al. (2017), McFadden et al. (2014), Chahal et al. (2013).

24

Appendix L. Summary for the risk of bias assessments

Table L1. Summary for the risk of bias assessments on the evidence of the relationship between LAN exposure and risk of obesity.

Sele

ctio

n bi

as

Con

foun

ding

bia

s

Attr

ition

/exc

lusi

on

bias

Det

ectio

n bi

as fo

r ex

posu

re

Det

ectio

n bi

as fo

r ou

tcom

e

Sele

ctiv

e re

porti

ng b

ias

Oth

er b

ias

Longitudinal studies

Obayashi et al. (2020)

Park et al. (2019)


Cross-sectional studies

Abay and Amare (2018)

Dube et al. (2017)

Koo et al. (2016)

McFadden et al. (2014)


Arora et al. (2013)

Chahal et al. (2013)

Calamaro et al. (2012)

Giammattei et al. (2003)

Level of risk of biasDefinitely Low

Probably Low

Probably High/Not reported

Definitely High

25

+ + + + + + +

+ + + -- + + +

+ + + + + + +

NR + NR + + + +

+ + + -- + + +

+ + + + + + +

+ + + -- - + +

+ + + + + + +

+ + + -- + + +

+ + + -- + + +

+ + + -- -- + +

+ + + -- + + +

++ + - NR --

Appendix M. Summary for assessments of risk of bias of individual studies – Cohort studies

Table M1. Risk of bias summary for Obayashi et al. (2020).Risk of bias domain

Authors’ judgement

Support for judgement

Selection bias Probably Low - Recruited community-dwelling elderly individuals aged ≥60 years with the cooperation of local residents’ associations and elderly residents’ clubs in Nara, Japan

Confounding bias Probably Low - propensity score using age, sex, smoking and drinking habit, education, household income, body mass index, hypertension, caloric intake, daytime

- physical activity, bedtime, rise time, and daytime light exposure, actigraphic total sleep time and sleep efficiency

Attrition/exclusion bias

Probably Low - 678 participants who participated in the follow-up study/954 participants who had no diabetes at baseline (71.1%)

Detection bias for exposure

Probably Low - The average light intensity measured by a portable light meter and recorded between bedtimes and rise times over two consecutive nights

Detection bias for outcome

Probably Low - Diabetes mellitus based on their medical history, current diabetes treatment, or HbA1c level 6.5%

Selective reporting bias

Probably Low - No selective reporting bias is suspected

Other bias Probably Low - No other bias is suspected

26

Table M2. Risk of bias summary for Park et al. (2019).Risk of bias domain



Selection bias Probably Low - subjects were similar, recruited within the same time frame

- methods of recruitment include word-of-mouth and flyer distribution through varying organizations, study participants and contacts made at local and national women’s events;

- other methods of recruitment include outreach through hospitals, mammography centers, churches, unions, and trade organizations; andmeans of media

Confounding bias Probably Low - ‘Potential confounders, mediators, or effect modifiers were identified a priori based on literature review and presumed causal relationships among the covariates’ (Park et al., p. E3)


Probably Low - Response rates throughout follow-up: >90%


Definitely High - Self-reported types of LAN that were usually present while sleeping at enrollment


Probably Low - ‘Self-reported weights at baseline were highly correlated with measured weights’ (Park et al., p. E2)




27

Table M3. Risk of bias summary for Obayashi et al. (2016).Risk of bias domain




Confounding bias Probably Low - age, sex, follow-up duration, smoking and drinking status, household income, education, caloric intake, physical activity, bedtime, duration in bed, and day length


Probably Low - baseline characteristics stratified by thefollow-up status are shown – no substantial difference


Probably Low - Light exposures measured by a portable light meter during the evening, nighttime, in the first (1 hour after bedtime) and last hour of the night (1 hour before rising time) for two consecutive days


Probably Low - Waist circumference, body weights, and heights were measured




28

Appendix N. Summary for assessments of risk of bias of individual studies – Cross-sectional studies

Table N1. Risk of bias summary for Abay and Amare (2018).Risk of bias domain



Selection bias Not reported - ‘The final sample comprises 18,667 women from the 2008 survey and 14,919 women from the 2013 survey.’

- ‘The DHS datasets are not panel surveys that follow the same households; rather, they are repeated cross-sections with a possibility offollowing the same clusters’ (Abay and Amere, 2018)

Confounding bias Probably Low - ‘We follow previous research to guide our choice of control variables’ (Abay and Amere, p. 240)


Not reported - No detailed information on criteria for exclusion


Probably Low - Nightlight intensity measured at the cluster level and for various time periods (data sourced from the DMSP)


Probably Low - the NDHS took height and weight measurements




29

Table N2. Risk of bias summary for Dube et al. (2017).Risk of bias domain



Selection bias Probably Low - 181 geographically representative elementary school in Alberta, Canada

- Reponse rate of the schools: 77.9%Confounding bias Probably Low - sex of the child, household income, region of

elementary school, highest level of parental education and total daily exposure to electronic entertainment and communication devices


Probably Low - included those who completed the school-based questionnaire and had heights and weights measured


Definitely High - Self-reported (by children) frequency of bedroom use of electronic entertainment and communication devices during an hour before sleep at night


Probably Low - Measured height and weight




30

Table N3. Risk of bias summary for Koo et al. (2016).Risk of bias domain



Selection bias Probably Low - Ansan: Random selection from directory listings;

- Ansung: door-to-door and telephone solicitations within 5 randomly selected political regions from a total of 11 regions

Confounding bias Probably Low - age, sex, education level, type of residential building, monthly household income, alcohol consumption of caffeine or alcohol before sleep, delayed sleep pattern, short sleep duration and habitual snoring


Probably Low - excluded participants with missing data on height, body weight or residential area


Probably Low - Yearly average nightlight data from 2001 and 2002 derived from the DMSP






31

Table N4. Risk of bias summary for McFadden et al. (2014).Risk of bias domain



Selection bias Probably Low - (1) registered supporters of the Breakthrough Breast Cancer charity (3.8%)

- (2) women who referred themselves to the study (22.9%)

- (3) friends and family nomination (majority)- 47% have returned the questionnaire and

become study membersConfounding bias Probably Low - age, having a child under age 5 years,

socioeconomic status, night-shift work in the previous 10 years, strenuous physical activity, alcohol consumption, sleep duration, and current smoking status


Probably Low - Included if participants reported anthropometric details and information about LAN exposure or sleeping patterns in the recruitment questionnaire.


Definitely High - Self-reported lightness of the room subjects slept in


Probably High - ‘The participants completed a detailed baseline postal questionnaire that included a comprehensive assessment of breastcancer risk factors, including weight, height, and waist and hip circumferences, and whether these factors were measuredon the day of the questionnaire, had been measured recently, or were estimated.’ (p. 246)




32

Table N5. Risk of bias summary for Obayashi et al. (2013).Risk of bias domain




Confounding bias Probably Low - ‘In the multivariate model, ORs were adjusted simultaneously for demographic and socioeconomic parameters, including age, sex, current smoking, habitual drinking, current income, and past education’ (p. 339)


Probably Low - Home-dwelling individuals at least 60 yr old with complete LAN measurements were included


Probably Low - Light exposures measured by a portable photometer during the bed-in-time to bed-out-time by a self-reported sleep diary


Probably Low - Measured waist circumference, weight and height and collected overnight fasting venous blood samples




33

Table N6. Risk of bias summary for Arora et al. (2013).Risk of bias domain



Selection bias Probably Low - Data were drawn from six schools across the UK Midlands region

- Response rate: 72.8%Confounding bias Probably Low - All potential confounders were selected

a priori, based on scientific evidence.Attrition/exclusion bias

Probably Low - Excluded cases who was either absent or attending curricular/sporting activities during data collection, did not provide written consent, had a diagnosed sleep disorder, were taking sleep medication or had travelled to a different time zone 4 weeks before data collection


Definitely High - Self-reported (by young people) technology use (computer use, mobile telephones, TV viewing, video gaming) at bedtime






34

Table N7. Risk of bias summary for Chahal et al. (2013).Risk of bias domain



Selection bias Probably Low - One stage stratified random sampling design; all elementary schools in Alberta with grade 5 students were included in the sampling frame; schools were stratified according to metropolitan, city or rural-town regions, and randomly selected within each stratum to ensure proportional representation of schools from each geographic region

Confounding bias Probably Low - sex, household income, parental education and area of residence


Probably Low - Grade 5 students studying in Alberta who agreed to participate in the study (151/164 schools).


Definitely High - Self-reported (by children) use of electronic entertainment and communication devices after the time the subjects were normally expected to go to sleep






35

Table N8. Risk of bias summary for Calamaro et al. (2012).Risk of bias domain



Selection bias Probably Low - Telephone surveys using a purchased list of telephone numbers targeted as households with children (the survey was designed to randomly select samples to achieve nationwide representation proportion to census data)

Confounding bias Probably Low - age, sex, race and general healthAttrition/exclusion bias

Probably Low - Inclusion: school children from the ages of 6 to 10 years.

- Exclusion: Seven children with missing data or zero hours reported for sleep duration in a typical night were excluded.


Definitely High - Self-reported (by children) television watching on an average school night


Definitely High - Self-reported height and weight




36

Table N9. Risk of bias summary for Giammattei et al. (2003).Risk of bias domain



Selection bias Probably Low - Letters sent to three schools in Santa Barbara County

Confounding bias Probably Low - age, sex, ethnicity, consumption of soft drinks per day


Probably Low - Inclusion: present in class on at least 1 of the study days and participated in data collection (405 out of 808 students students).

- Exclusion: older than 14 years, confined to a wheelchair and subjects who are not non-Hispanic white, Latino or Asian.


Definitely High - Self-reported (by children) television watching on an average school night






37

Appendix O.

Table O1. Summary for the assessments of confidence in the body of evidence of the relationship between LAN exposure and risk of obesity.

Category Grading RationaleDowngrading domains Risk of bias -1 Detection bias for outcome was rated as ‘definitely high’

in seven out of 12 studies. Biases with respect to selection, exclusion and outcome were also found in several studies. Therefore, a judgement of downgrading was produced.

Indirectness 0 Evidence in terms of population, exposure, comparator and outcome (PECO) was comparable to the research question.

Inconsistency 0 A low level of heterogeneity (I2 = 27.27%) was observed in studies examining the associations between LAN exposure and overweight, but substantial heterogeneity was evident in the results relating LAN exposure and obesity (I2 = 85.96%).

Imprecision 0 Only a few studies showed relatively wide confidence interval as evident from the forest plots, so a judgement of downgrading was not permitted.

Publication bias 0 Linear regression test for funnel plot asymmetry showed an insignificant result (p = 0.20) in studies examining an association of LAN exposure with overweight, but a significant result was evident with obesity (p = 0.01). Due to the small number of studies pooled, a judgement of downgrading was not permitted (Appendix P).

Upgrading domain Effect magnitude 0 Large magnitude of the association between LAN

exposure and risk of obesity was not evident. Dose-response 0 A dose-response relationship between LAN exposure and

risk of obesity was not evident. Confounding 0 No spurious effect was suspected.

38

Appendix P. Funnel plots for assessments of publication bias

Figure P1. Funnel plot of the seven studies in the association between LAN exposure and overweight (body mass index≥25kg/m2) (linear regression test for funnel plot asymmetry: p = 0.20). Note. Logarithms of odds ratios versus the inverse of standard errors (1/SE).

39

Figure P2. Funnel plot of the five studies in the association between LAN exposure and obesity (body mass index≥30kg/m2) (linear regression test for funnel plot asymmetry: p = 0.01). Note. Logarithms of odds ratios versus the inverse of standard errors (1/SE).

40

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