手法を比較

	モバイルセンサーデータ収集	縦断的センサーデータ収集
分野	調査方法論	調査方法論
系統	Process / pipeline	Process / pipeline
提唱年	Mid-2000s (smartphone-era formalization ~2006–2010)	1990s–2000s (accelerated with IoT and wearable devices from ~2010)
提唱者	Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing	Emerging from ambulatory assessment and wearable technology research communities
種類	Passive and active quantitative data collection technique	Longitudinal quantitative/mixed data collection technique
原典	Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.	Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2005). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14(4), 671–694. [For longitudinal intensive repeated-measures designs context, see also: Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.]
別名	mobile sensing, smartphone sensor data collection, wearable sensor data collection, passive mobile data collection	long-term sensor monitoring, longitudinal sensing, continuous sensor logging, repeated-measures sensor collection
関連	4	3
概要	Mobile sensor data collection uses the built-in sensors of smartphones, tablets, or wearable devices to capture behavioral, physiological, and environmental data in real-world settings. Sensors such as accelerometers, GPS, heart rate monitors, ambient light detectors, and microphones record data passively or on demand, enabling researchers to study human behavior with high temporal resolution outside the laboratory.	Longitudinal sensor data collection deploys physical or digital sensors to record phenomena continuously or at regular intervals across an extended study period — days, months, or years. Unlike one-shot measurement, the repeated temporal structure captures change, trajectory, and variability in outcomes such as physical activity, environmental exposure, sleep, or physiological state. The approach combines the ecological validity of real-world sensing with the analytical power of longitudinal design.