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Identifying saltatory growth patterns in infancy: A comparison of results based on measurement protocol
Michelle Lampl1, Michael L Johnson2
1Department of Anthropology, Emory University, Atlanta, Georgia 30322.
Insights
Frequent infant growth measurements are crucial for identifying saltatory growth patterns. Daily or every-other-day measurements are needed, as less frequent data collection can lead to misperceptions of growth.
Area of Science:
- Human Biology
- Pediatric Growth Studies
Background:
- Infant growth is characterized by pulsatile, saltatory patterns.
- Accurate identification of these patterns requires sufficient data frequency.
Purpose of the Study:
- To determine the optimal frequency for growth measurements to statistically identify infant saltatory growth patterns.
- To compare the efficacy of different data collection intervals for growth analysis.
Main Methods:
- Analyzed daily serial growth measurements from three infants over 4 months.
- Created seven data subsets with decreasing data points (daily to weekly intervals).
- Utilized a saltatory algorithm and continuous curvilinear models to compare growth pattern identification across data sets.
Main Results:
- Statistically significant identification of growth pulses and resolution of saltatory patterns decreased with measurement intervals greater than 2-5 days.
- Infrequent protocols (e.g., Monday, Wednesday, Friday) may not adequately capture individual infant saltatory growth.
- Dependence on infrequent data introduces significant error, potentially misperceiving growth patterns.
Conclusions:
- Daily or near-daily growth measurements are essential for accurate characterization of infant saltatory growth.
- Less frequent measurement schedules compromise the reliability of growth pattern analysis.
- Careful consideration of data collection frequency is vital to avoid misinterpretation of infant growth dynamics.
Abstract:
The present analyses address the question of how frequently time-intensive growth measurements need to be taken in order to provide sufficient data for the statistical identification of pulsatile saltatory growth patterns during infancy. The daily serial growth measurements of three infants during 4 months are analyzed in seven subsets, which include progressively fewer data points from daily to weekly intervals. Saltatory growth pulse identification and growth pattern analyses are compared between the temporally distinctive data sets by the saltatory algorithm and continuous curvilinear models. Statistically significant pulse identification and saltatory growth pattern resolution decrease with time intervals > 2-5 days in these data sets. These results suggest that the convenience of a Monday, Wednesday, Friday protocol may not be sufficient to characterize saltatory growth in individual infants, and the dependability factor is a significant source of error that may contribute to growth pattern misperception in data collected by infrequent protocols. Am. J. Hum. Biol. 9:343-355, 1997. © 1997 Wiley-Liss, Inc.
