"Social Acceptance Among Peers and Musculoskeletal Pain in Adolescents"

Few studies have investigated social factors in association with musculoskeletal pain in adolescents, such as perceived peer acceptance. Adolescence is a vulnerable period of life when peer relations is important in order to develop social, emotional and behavioural skills. Social relations have been linked to several health outcomes in previous studies. Therefore, this study aims to investigate if social acceptance among peers is associated with musculoskeletal pain in adolescents.

The research questions are:

1. Is there a significant association between perceived social acceptance among peers during the first year of high school and persistent MSK pain two years later in adolescents?
2. Is psychological distress an effect modifier of the relationship between perceived social acceptance among peers during the first year of high school and MSK pain two years later in adolescents?

Method:

This is a prospective cohort study using data from the Fit Futures (FF) study. Students from the first year of high school were included at baseline in Fit Futures 1 (FF1) and followed up for two years later in Fit Futures 2 (FF2). The students answered a comprehensive questionnaire, including biological, psychological, and social variables. They also underwent measurements of height and weight during school hours.

Baseline characteristics will be extracted from FF1 with data collected in the period 2010-2011. Baseline characteristics from adolescents who participated in the study and adolescents who were lost to follow-up will be presented and compared. Information about age, sex, parents socioeconomic status (SES), and body mass index (BMI), will be used to describe the study sample and as potential confounders.

Social acceptance among peers was measured by five questions from the revised Norwegian version of Self-perception Profile for Adolescent; scale for social competence and psychological distress was measured by the Hopkins Symptoms Checklist-10.

Statistical analyses:

All statistical tests will be two-sided, and the nominal p-values will be reported. All confidence intervals (CIs) will be reported as 95%. Preliminary analyses of frequencies, missing data and normality will be conducted. The assumption of normal distribution will be investigated using histograms and QQ-plots. Normally distributed data will be described with means and standard deviations (SDs), skewed data will be presented with medians and ranges. Categorical data will be reported as counts and percentages. All statistical analyses will be conducted using SPSS statistical software (SPSS Inc, Chicago, IL, USA).

Participants with missing data on the outcome will be excluded from the analyses. Missing data on the exposure variable will be handled by multiple imputations unless the missingness is very low (<10%). A two-year incidence rate of participants who reports no MSK pain at baseline and develops persistent MSK pain during the follow-up period will be calculated, presented as percentage with 95% CI.

Logistic regression:

Main analyses will be conducted in a sample of adolescents that report no persistent MSK pain at baseline. Secondary analyses will be conducted in the total sample regardless of pain status at baseline. Binary logistic regression will be conducted with social acceptance among peers as the exposure and persistent MSK pain as the outcome. Sex and chronic diseases will be included as confounders.

Moderation analyses:

The moderation analysis will be performed as described by Hayes, using PROCESS macro model 1 in SPSS. In the analysis, social acceptance will be included as the exposure, MSK pain as the outcome, and psychological distress as the possible effect modifier. Sex and chronic diseases will be included as confounders. Regression coefficients and ORs will be presented in figures and tables. Confidence intervals for the effect modifier will be constructed using a bias-corrected bootstrap method with 5000 bootstrap samples.

Additional analyses:

Drop-out analyses will be conducted to explore possible attrition bias by comparing baseline characteristics between participants lost to follow-up and responders at follow-up. The chi-square test will be used for comparison of categorical variables, independent samples t-test to compare normally distributed continuous variables, and Mann-Whitney U test to compare data with skewed distribution.