2009
DOI: 10.5432/ijshs.ijshs20070289
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Verification Regarding Secular Trend of Height Growth and The Maximum Peak Velocity during Adolescence

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Cited by 2 publications
(3 citation statements)
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“…When constructing an O model multi-year span evaluation chart, it is important to prepare an evaluation line that distinguishes between evaluation bands. Until recently, the method of analyzing trends over years has been to apply a least squares approximation polynomial devised by Fujii [13], but it had the problem that it did not go through the observation points, and so while it was sufficiently effective for seeing simple trends, in this study we decided to apply the wavelet interpolation model, which can pass through the observation points, rather than the least squares approximation since this study had a relatively short span of seven years. The reason for applying the wavelet interpolation model is that with first-degree functions only linearly increasing compositions can be verified, but with second degree or higher functions it is thought that the composition of the changes in increases can be captured in detail.…”
Section: Discussionmentioning
confidence: 99%
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“…When constructing an O model multi-year span evaluation chart, it is important to prepare an evaluation line that distinguishes between evaluation bands. Until recently, the method of analyzing trends over years has been to apply a least squares approximation polynomial devised by Fujii [13], but it had the problem that it did not go through the observation points, and so while it was sufficiently effective for seeing simple trends, in this study we decided to apply the wavelet interpolation model, which can pass through the observation points, rather than the least squares approximation since this study had a relatively short span of seven years. The reason for applying the wavelet interpolation model is that with first-degree functions only linearly increasing compositions can be verified, but with second degree or higher functions it is thought that the composition of the changes in increases can be captured in detail.…”
Section: Discussionmentioning
confidence: 99%
“…The characteristics of the wavelet interpolation model are that local phenomena are read sensitively and the approximation accuracy is very high. The details of the theoretical background and the grounds for its effectiveness have already been described in Fujii's previous research [11][12][13], so the data analysis algorithm using the wavelet interpolation model is omitted here. In this study, especially, the wavelet interpolation model was applied to the age distance value of annual trend of physical fitness from 2013 to 2019 years in second year girls' junior high school (seventh grade) students.…”
Section: Wavelet Interpolation Model (Wim)mentioning
confidence: 99%
“…Kikuta et al sought the age at which the annual amount of height growth was the highest, in other words, the age at peak height velocity (PHV) in puberty, and from analysis of the time trends in that age reported that the age at which PHV appeared was getting younger [4]. Fujii and Fujii et al took this research a step further and identified the age at maximum peak velocity (MPV) in height using a wavelet interpolation model and verified the growth acceleration phenomenon from secular trends in age at MPV [5,6]. However, in 1973, when Japan's high economic growth was coming to an end, the phenomenon of promoted growth was also gradually tapering off, and from that time until today no clear changes has been seen in the age at MPV of height.…”
Section: Introductionmentioning
confidence: 99%