Member of Boy Band Rewind Collapses on Plane Due to 12 Layers of Clothing(?)

Hopefully all attention is good attention if you're an up-and-coming boy band, because this headline gets ridiculous real fast: James McElvar, a member of the Scottish pop group Rewind, collapsed due to heat exhaustion after trying to wear ALL of his clothing in an attempt to avoid paying luggage fees. Yup.

McElvar, traveling alone apparently, was flying from Stansted in Essex back to the band's hometown of Glasgow over the weekend, when he was informed that his carry-on luggage was just too big, and he would need to pay an additional £45 in fees (around $70) for the extra baggage. Thinking quickly, if not efficiently, McElvar decided to remove all of the clothing and wear it onto the plane.

The clothing breaks down into six T-shirts, five sweaters, two jackets, three pairs of jeans, two pairs of sweatpants and two hats. Yes, 13 layers of clothing on his upper body, along with five for his lower body. Unsurprisingly (despite taking off most of the clothing as soon as the plane was in the air), McElvar began to suffer from intense heat exhaustion shortly after. Frankly, if he were wearing that much clothing in Barrow, AK, he would still suffer from heat exhaustion.

"I thought I was a goner and that I was having a heart attack," he told The Sun in what was likely the most embarrassing and awkward interview of his life.

Fortunately, there happened to be a paramedic traveling on the same flight, who managed to stabilize the afflicted pop performer.

The official Twitter account of the group confirmed that McElvar came out of the incident no worse-for-wear. Except the embarrassment part, of course.

"For everyone asking James is ok. He is being looked after in hospital now back home safe in Glasgow. He sends you all his love," the band posted, followed by an update the next morning: "James is much better now! Still in a bit of shock but he's recovered from last night! And the other boys are fine and home safe!"

Join the Discussion
Real Time Analytics