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    ์‹์Šต๊ด€ ๊ด€๋ฆฌ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜๊ณผ ์‹์‚ฌ์ผ์ง€ ์‚ฌ์šฉ ๊ฐ„ ์ฒด์ค‘ ๊ฐ๋Ÿ‰ ๋น„๊ต: ๋ฌด์ž‘์œ„ ๋ฐฐ์ • ์ค‘์žฌ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ƒํ™œ๊ณผํ•™๋Œ€ํ•™ ์‹ํ’ˆ์˜์–‘ํ•™๊ณผ, 2019. 2. ์ด์ •์€.๋น„๋งŒ ์œ ๋ณ‘๋ฅ ์€ ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๋Š” ์ถ”์„ธ์ด๋ฉฐ, ์ด๋ฅผ ์˜ˆ๋ฐฉํ•˜๊ธฐ ์œ„ํ•œ ํšจ๊ณผ์ ์ธ ์ค‘์žฌ ์ „๋žต์ด ์š”๊ตฌ๋œ๋‹ค. ์„ ํ–‰๋œ ์ค‘์žฌ์—ฐ๊ตฌ๋“ค์—์„œ ์‹์Šต๊ด€ ๊ด€๋ฆฌ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์ด ์ฒด์ค‘ ๊ฐ๋Ÿ‰ ๋ฐ ์‹์‚ฌ ๊ด€๋ฆฌ ๋„๊ตฌ๋กœ์„œ ํšจ๊ณผ๊ฐ€ ์žˆ๋Š”์ง€ ํ‰๊ฐ€ํ•˜์˜€์œผ๋ฉฐ, ์—ฐ๊ตฌ์˜ ์„ค๊ณ„ ๋ฐ ์‚ฌ์šฉ๋œ ๋„๊ตฌ์— ๋”ฐ๋ผ ๊ทธ ํšจ๊ณผ ์—ฌ๋ถ€๊ฐ€ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ฒด์งˆ๋Ÿ‰์ง€์ˆ˜ (BMI)๊ฐ€ 23 kg/m2 ์ด์ƒ์ธ ์ Š์€ ์„ฑ์ธ๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ๋ฌด์ž‘์œ„ ๋ฐฐ์ • ์ค‘์žฌ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ 6์ฃผ ๋™์•ˆ ์Šค๋งˆํŠธํฐ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ ์‚ฌ์šฉ๊ตฐ๊ณผ ์‹์‚ฌ ์ผ์ง€ ์‚ฌ์šฉ๊ตฐ ๊ฐ„ ์ฒด์ค‘ ๊ฐ๋Ÿ‰ ํšจ๊ณผ๋ฅผ ๋น„๊ตํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋Œ€์ƒ์ž ์ฐธ์—ฌ ๊ธฐ์ค€์€ 1) ๋งŒ ์—ฐ๋ น 18์„ธ ์ด์ƒ 40์„ธ ์ดํ•˜์˜ ์„ฑ์ธ, 2) BMI 23 kg/m2 ์ด์ƒ์ธ ์ž, 3) ์ฒด์ค‘ ๊ฐ๋Ÿ‰ ์˜์ง€๊ฐ€ ์žˆ๋Š” ์ž, 4) ์ž„์‹ ๋ถ€ ๋ฐ ์ˆ˜์œ ๋ถ€๊ฐ€ ์•„๋‹Œ ์ž, 5) ์Šค๋งˆํŠธํฐ ์‚ฌ์šฉ์ž๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๊ฒฐ๊ณผ ๋ณ€์ˆ˜๋กœ์„œ ์ฒด์ค‘(kg), BMI(kg/m2), ํ—ˆ๋ฆฌ๋‘˜๋ ˆ(cm), ์ฒด์ง€๋ฐฉ๋Ÿ‰(kg), ๊ณจ๊ฒฉ๊ทผ๋Ÿ‰(kg)์„ ์กฐ์‚ฌํ•˜์˜€๊ณ  ๋ณด์กฐ ๊ฒฐ๊ณผ ๋ณ€์ˆ˜๋กœ์„œ ์˜์–‘์†Œ ์„ญ์ทจ ๋ณ€ํ™” ๋ฐ ์ค‘์žฌ ๊ธฐ๊ฐ„ ์ค‘ ์—ฐ๊ตฌ ์ฐธ์—ฌ๋„๋ฅผ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ค‘์žฌ ๊ธฐ๊ฐ„ ๋™์•ˆ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜๊ตฐ์€ ๋ณธ ์—ฐ๊ตฌ์ง„์ด ๊ฐœ๋ฐœํ•œ ์‹์Šต๊ด€ ๊ด€๋ฆฌ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ Well-D๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์„ญ์ทจํ•œ ์Œ์‹ ๋ฐ ์‹์ด ๋ณด์ถฉ์ œ๋ฅผ ๊ฒ€์ƒ‰ํ•˜์—ฌ ๊ธฐ๋กํ–ˆ๊ณ , ์ฐธ์—ฌ์ž์˜ ํ”„๋กœํ•„๊ณผ ์‹์‚ฌ์„ญ์ทจ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ฆ‰๊ฐ์ ์ธ ์˜์–‘์†Œ ๋ฐ ์‹ํ’ˆ ํ”ผ๋“œ๋ฐฑ์„ ์ œ๊ณต๋ฐ›์•˜๋‹ค. ์‹์‚ฌ ์ผ์ง€๊ตฐ์€ ์‹์‚ฌ ์ผ์ง€๋ฅผ ์ด์šฉํ•ด ๋‚ ์งœ, ์‹œ๊ฐ„ ๋ฐ ์„ญ์ทจํ•œ ์Œ์‹, ์žฌ๋ฃŒ, ๋ถ„๋Ÿ‰, ์—ด๋Ÿ‰, ์‹์ด ๋ณด์ถฉ์ œ๋ฅผ ๊ธฐ๋กํ•˜์˜€๊ณ , ์ฒด์ค‘ ๊ฐ๋Ÿ‰์„ ์œ„ํ•œ ๋ชฉํ‘œ ์—ด๋Ÿ‰์„ ์ง์ ‘ ๊ณ„์‚ฐํ•˜์—ฌ ์‹ค์ œ ์„ญ์ทจ ์—ด๋Ÿ‰๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค. ์‹์‚ฌ ์ผ์ง€์™€ ๋”๋ถˆ์–ด ์ฒด์ค‘ ๊ฐ๋Ÿ‰ ์ „๋žต์ด ํฌํ•จ๋œ ๋ฆฌํ”Œ๋ฆฟ์„ ํ•จ๊ป˜ ์ œ๊ณต๋ฐ›์•˜๋‹ค. ๊ตฐ ๊ฐ„ ์—ฐ๊ตฌ๋Œ€์ƒ์ž์˜ ํŠน์„ฑ ๋ฐ ๊ฒฐ๊ณผ๋ณ€์ˆ˜์˜ ํผ์„ผํŠธ ๋ณ€ํ™” ์ฐจ์ด(%)๋Š” ์—ฐ์†ํ˜• ๋ณ€์ˆ˜์˜ ๊ฒฝ์šฐ independent t-test ๋˜๋Š” Wilcoxon Mann-Whitney test๋กœ ๋ถ„์„ํ•˜์˜€๊ณ , ๋ฒ”์ฃผํ˜• ๋ณ€์ˆ˜์˜ ๊ฒฝ์šฐ Chi-squre test ํ˜น์€ Fishers exact test๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฐ ๊ตฐ ๋‚ด ์ค‘์žฌ ์ „ํ›„ ์ธก์ •์น˜ ๋ณ€ํ™” ์ฐจ์ด๋Š” ์กฐ์‚ฌ๋œ ์ธก์ •์น˜๋ฅผ Box-cox analysis๋กœ ๋ณ€ํ™˜ํ•œ ๋’ค paired t-test ํ˜น์€ Wilcoxon rank sum test๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ฐธ์—ฌ์ž์˜ ์—ฐ๊ตฌ ์ˆœ์‘๋„์— ๋”ฐ๋ฅธ ์ฒด์ค‘ ๋ณ€ํ™”๋ฅผ simple linear regression์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ฐ์ดํ„ฐ๋Š” Intention to treat analysis๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ๊ฒฐ์ธก๊ฐ’์€ ์ด์›”๋Œ€์ฒด(carried forward method)ํ•˜์˜€๊ณ  ๋ฏผ๊ฐ์„ฑ ๋ถ„์„์œผ๋กœ์„œ per-protocol analysis์™€ ๋‹ค์ค‘๋Œ€์ฒด๋ฒ•์„ ์ด์šฉํ•ด(multiple imputation by chained equations) ์ค‘์žฌ ํ›„ ์‹ ์ฒด๊ณ„์ธก์น˜์™€, ์—ด๋Ÿ‰, ํƒ„์ˆ˜ํ™”๋ฌผ, ๋‹จ๋ฐฑ์งˆ, ์ง€๋ฐฉ์˜ ๊ฒฐ์ธก๊ฐ’์„ ๋Œ€์ฒดํ•œ ๋’ค ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜๊ตฐ ๋ฐ ์‹์‚ฌ์ผ์ง€๊ตฐ์œผ๋กœ์„œ ๊ฐ๊ฐ 25๋ช…์˜ ์ฐธ์—ฌ์ž๊ฐ€ ๋ฐฐ์ •๋˜์—ˆ๊ณ  6์ฃผ ๊ฐ„์˜ ์ค‘์žฌ์—ฐ๊ตฌ์— ์ฐธ์—ฌํ•˜์˜€๋‹ค. ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜๊ตฐ์˜ ์ฐธ์—ฌ์ž๋Š” ๋ชจ๋‘ ์ค‘์žฌ๋ฅผ ๋งˆ์นœ ๋’ค ๋ฐฉ๋ฌธํ•˜์—ฌ ์ค‘์žฌ ํ›„ ์กฐ์‚ฌ์— ์ฐธ์—ฌํ•˜์˜€๊ณ , ์‹์‚ฌ์ผ์ง€๊ตฐ ์ค‘ 23๋ช…์˜ ์ฐธ์—ฌ์ž๊ฐ€ ์ค‘์žฌ ํ›„ ์กฐ์‚ฌ๋ฅผ ์™„๋ฃŒํ•˜์˜€๋‹ค. ์ตœ์ข…์ ์œผ๋กœ ๊ฐ ๊ตฐ๋ณ„ 25๋ช…์„ ๋ถ„์„์— ํฌํ•จํ•˜์˜€๋‹ค. ๊ธฐ์ €์กฐ์‚ฌ์—์„œ ์—ฐ๊ตฌ์ฐธ์—ฌ์ž ๊ฐ„ ํŠน์„ฑ ์ฐจ์ด๋Š” ์œ ์˜ํ•˜์ง€ ์•Š์•˜๋‹ค. ์ฒด์ค‘์„ ํฌํ•จํ•œ ๋ชจ๋“  ์ธก์ •์น˜์˜ ๊ตฐ ๊ฐ„ ํผ์„ผํŠธ ๋ณ€ํ™” ์ฐจ์ด๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜์ง€ ์•Š์•˜๋‹ค (์ฒด์ค‘ % ๋ณ€ํ™”์˜ ํ‰๊ท  ยฑ ํ‘œ์ค€ํŽธ์ฐจ: -0.12ยฑ0.52 vs โ€“0.39ยฑ0.73p-value=0.24). ์ค‘์žฌ ์ „ํ›„ ์‹์‚ฌ์ผ์ง€๊ตฐ ๋‚ด์—์„œ ์ฒด์ค‘๊ณผ BMI๊ฐ€ ์œ ์˜ํ•˜๊ฒŒ ๊ฐ์†Œํ•˜์˜€๋‹ค. ํ—ˆ๋ฆฌ๋‘˜๋ ˆ, ์ฒด์ง€๋ฐฉ๋Ÿ‰์€ ๋‘ ๊ตฐ ๋‚ด์—์„œ ๋ชจ๋‘ ์œ ์˜ํ•˜๊ฒŒ ๊ฐ์†Œํ•˜์˜€์œผ๋‚˜, ๊ณจ๊ฒฉ๊ทผ๋Ÿ‰์€ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜๊ตฐ ๋‚ด์—์„œ๋งŒ ์œ ์˜ํ•˜๊ฒŒ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๋‘ ๊ตฐ ๋ชจ๋‘ ์—ฐ๊ตฌ ์ฐธ์—ฌ ์ˆœ์‘๋„์— ๋”ฐ๋ผ ์ฒด์ค‘์ด ๋‚ฎ์•„์ง€๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์—ˆ์œผ๋‚˜ ๊ทธ ๋ณ€ํ™”๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜์ง€ ์•Š์•˜๋‹ค. ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜๊ตฐ๊ณผ ์‹์‚ฌ์ผ์ง€๊ตฐ ๊ฐ„ ์˜์–‘์†Œ ํผ์„ผํŠธ ๋ณ€ํ™”์— ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์—†์—ˆ๊ณ , ๋‘ ๊ตฐ ๋‚ด์—์„œ ๋ชจ๋‘ ์ค‘์žฌ ์ „ํ›„ ์—๋„ˆ์ง€ ์„ญ์ทจ๋Ÿ‰์ด ๊ฐ์†Œํ–ˆ๋‹ค (๊ฐ p-value=0.04, 0.06). ์‹์‚ฌ์ผ์ง€๊ตฐ ๋‚ด์—์„œ ์ค‘์žฌ ์ „ํ›„ ํƒ„์ˆ˜ํ™”๋ฌผ, ์ฝœ๋ ˆ์Šคํ…Œ๋กค, ์นผ์Š˜, ์ธ, ์ฒ ๋ถ„, ์นผ๋ฅจ, ํ‹ฐ์•„๋ฏผ ์„ญ์ทจ๋Ÿ‰์˜ ์œ ์˜ํ•œ ๊ฐ์†Œ๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋ฏผ๊ฐ์„ฑ ๋ถ„์„ ์ˆ˜ํ–‰ ๊ฒฐ๊ณผ ๊ฒฝํ–ฅ์˜ ๋ณ€ํ™”๊ฐ€ ์—†์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณผ์ฒด์ค‘ ๋ฐ ๋น„๋งŒ ์„ฑ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์Šค๋งˆํŠธํฐ ์‹์Šต๊ด€ ๊ด€๋ฆฌ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์ด์šฉํ•œ ์ฒด์ค‘ ๋ณ€ํ™” ํšจ๊ณผ๋ฅผ ์‹์‚ฌ์ผ์ง€ ์‚ฌ์šฉ๊ณผ ๋น„๊ตํ•˜์—ฌ ํŒŒ์•…ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜๊ตฐ๊ณผ ์‹์‚ฌ์ผ์ง€๊ตฐ ๊ฐ„ ์‹ ์ฒด๊ณ„์ธก์น˜ ๋ฐ ์˜์–‘์†Œ ํผ์„ผํŠธ ๋ณ€ํ™”์— ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜๊ตฐ ๋‚ด์—์„œ ํ—ˆ๋ฆฌ๋‘˜๋ ˆ, ์ฒด์ง€๋ฐฉ๋Ÿ‰ ๋ฐ ์—ด๋Ÿ‰์„ญ์ทจ๋Ÿ‰์˜ ์œ ์˜ํ•œ ๊ฐ์†Œ์™€ ๊ณจ๊ฒฉ๊ทผ๋Ÿ‰์˜ ์œ ์˜ํ•œ ์ฆ๊ฐ€๊ฐ€ ์žˆ์—ˆ์œผ๋ฉฐ, ์‹์‚ฌ์ผ์ง€๊ตฐ ๋‚ด์—์„œ ์ฒด์ค‘, BMI, ํ—ˆ๋ฆฌ๋‘˜๋ ˆ, ์ฒด์ง€๋ฐฉ๋Ÿ‰, ํƒ„์ˆ˜ํ™”๋ฌผ, ์ฝœ๋ ˆ์Šคํ…Œ๋กค, ์นผ์Š˜, ์ธ, ์ฒ ๋ถ„, ์นผ๋ฅจ, ํ‹ฐ์•„๋ฏผ ์„ญ์ทจ๋Ÿ‰์˜ ์œ ์˜ํ•œ ๊ฐ์†Œ๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ ์‚ฌ์šฉ๊ณผ ์‹์‚ฌ์ผ์ง€ ๋ชจ๋‘ ์‹ ์ฒด๊ณ„์ธก์น˜ ๋ณ€ํ™” ๋ฐ ์˜์–‘์†Œ ์„ญ์ทจ๋Ÿ‰ ๋ณ€ํ™” ๊ฐ€๋Šฅ์„ฑ์ด ์ œ์‹œ๋˜์—ˆ์œผ๋ฉฐ, ํ–ฅํ›„ ํšจ๊ณผ์ ์ธ ์‹์Šต๊ด€ ๊ด€๋ฆฌ ๋ฐฉ๋ฒ• ๋งˆ๋ จ ์‹œ ์ฐธ๊ณ ์ž๋ฃŒ๊ฐ€ ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ผ ์‚ฌ๋ฃŒ๋œ๋‹ค.Effective intervention strategies to maintain a balanced diet and healthy weight are warranted. Mobile health tools may have potential for dietary self-monitoring and assessment. This study aims to evaluate the effectiveness of a mobile dietary self-monitoring application (app) for weight loss compared to a paper-based diary among adults with a body mass index (BMI) of 23 kg/m2 or above. A total of 33 men and 17 women aged 18-39 years participated in a six-week randomized trial. This study randomly assigned participants to one of two groups: (A) a smartphone app group (n=25) or (B) a paper-based diary group (n=25). The smartphone app group recorded foods and dietary supplements that they consumed and received immediate dietary feedback using Well-D, a dietary self-monitoring app developed by our team. The paper-based diary group was instructed to record foods or supplements that they consumed using the self-recorded diary. The primary outcomes were weight, BMI, waist circumference, body fat mass and skeletal muscle mass. This study also examined changes in nutrient intakes including energy, carbohydrate, protein, fat, dietary fiber, vitamins, and minerals using 3-day 24-hour recalls across time at pre- and post-intervention. Differences between pre- and post-interventions within each group were compared using a paired t-test or the Wilcoxon signed rank test. Differences in changes between the two groups were analyzed using an independent t-test and the Wilcoxon Mann-Whitney test. The differences in changes of body weight, BMI, waist circumference, body fat mass, and skeletal muscle mass were not significantly different between the app group and the paper-based diary group. For pre- versus post-intervention measures, significant decreases in weight and BMI were observed in the paper-based diary group (p=0.02 and 0.01 respectively), but not in the app group (p=0.25 and 0.26 respectively). Waist circumference and body fat mass decreased significantly in both groups. The skeletal muscle mass significantly increased only in the app group. The percent changes in nutrient intakes were not statistically significant between the two groups. Energy intake decreased from pre- to post-intervention in the app group and the paper-based diary group. There were significant decreases in the intakes of carbohydrates, cholesterol, calcium, phosphorus, iron, potassium, and thiamin only in the paper-based diary group. In conclusion, there were no differences in changes of anthropometric measures and nutrient intakes between the app group and the paper-based diary group. Both mobile dietary self-monitoring app and paper-based diary may be useful for improving anthropometric measures and dietary intake.Abstract 1 List of Tables 5 List of Figures 6 List of Abbreviations 7 โ… . Introduction 8 โ…ก. Literature Review 10 1. Mobile health 10 2. Dietary assessment through smartphone apps 12 3. Dietary improvement with smartphone dietary apps 14 4. Weight loss studies of smartphone dietary apps 16 โ…ข. Subjects and Methods 19 1. Participants 19 2. Screening and Randomization 19 3. Intervention 20 3.1. Well-D app group 20 3.2. Paper-based diary group 28 4. Outcome assessments 29 4.1. Primary outcomes 29 4.2. Secondary outcomes 29 4.3. Other outcomes 30 5. Statistical Analysis 31 โ…ฃ. Results 33 1. Flow diagram of inclusion of study participants 33 2. Baseline characteristics of participants 35 3. Changes in anthropometric measures 37 4. Changes in nutrient intakes 41 5. Change in body weight according to the number of days of recording 44 6. Results of sensitivity analysis 46 7. Feasibility of the app 54 โ…ค. Discussion 56 References 62 ๊ตญ๋ฌธ์ดˆ๋ก 72Maste

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