Why do intensive longitudinal data (ILD) matter?
As data collection technology such as smart phones and pedometers create richer and denser datasets, methods for ILD are needed that allow researchers to answer new questions and to answer existing questions with greater nuance than was possible just a few years ago. Problem behaviors (e.g., substance use) and their predictors (e.g., craving, mood) change over time, and methods like TVEM and FHLM can help us understand these changes. This applies to smoking, obesity, substance use, HIV disease course, and any other area of behavioral science that collects ecological momentary assessment (EMA) data or other forms of ILD.
What are ILD?
There is no hard, fast rule for exactly what constitutes ILD. Generally, ILD are defined as data with more than 30 or 40 measurements over time. But in truth, it is not the number of observations that matter, it is the constructs and relationships you are trying to model, the speed at which they change, and whether you have sufficiently dense data to capture the change.
How are ILD collected?
and wearable technologies
have created a
plethora of possibilities
for the collection of ILD.
Intensive longitudinal data are collected in a variety of ways. These include but are not limited to
— daily diaries, where participants typically record data 1x/day
— ecological momentary assessments (EMA), where people are prompted to provide small amounts of data at numerous points throughout the day
— wearable sensors, which can provide near continuous streaming information throughout the course of one’s day on measures such as heart rate, step counts, and skin conductance.
Although the influx of mobile and wearable technologies have created a plethora of possibilities for the collection of ILD, interest in and collection of ILD are not strictly new endeavors. Prior to the introduction of smartphones and wearable devices, paper-and-pencil diaries, telephone calls, pagers, and palm pilots had been used by many researchers to collect ILD. Currently, smartphones, tablets, and wearable biosensors dominate the landscape of ILD collection. Smartphones and tablets can be used to obtain brief questionnaire responses and global positioning system (GPS) coordinates to better understand shifts in mood, behavior, social interaction, and context as individuals go through their daily lives, and a wide variety of wearable sensors are now available allowing real-time measurement of myriad dynamic biomarkers, including heart rate, motion and body position, step counts, skin conductance – even alcohol intoxication.
What kinds of questions can ILD answer?
Just as it covers a broad range of data types, ILD can be used to address a broad range of questions, many of which pertain to how contexts, moods, and behaviors change relative to themselves and relative to each other in people’s real everyday lives. Below are a few examples of questions which ILD are uniquely suited to answer.
— How does nicotine craving change over the course of a day among cigarette smokers?
— How does mood change over the course of the day on days with a lot of stressors compared to days with fewer?
— How does a person’s mood change leading up to — and throughout — a drinking episode?
These questions are valuable for examining change when data are collected intensively over a short time frame (between a few hours or a few days), the hallmark of ILD collection. Of course, these are only a handful of examples of questions that have been and are being asked using ILD. Importantly, emergent technologies and innovative analytic methods are constantly expanding the set of questions that ILD can answer.
How are ILD analyzed?
There are many ways one can analyze ILD. An important first step is to articulate your working model of change, which will then allow you to identify the analytic technique that may be best suited to allow that change to emerge.
For example, suppose you hypothesize (a) a simple, linear increase in drinking behavior on days when an individual is highly stressed compared to herself on days when she is not as highly stressed and (b) that the size of this increase will be larger for some people than it will be for others. Such an analysis may be performed using multilevel modeling, where the within-person association between stress and drinking can be estimated at the daily level, and between-person differences in the size of these associations can be estimated at the person level.
In another example, suppose you are interested in how mood changes in the hours following the onset of a drinking episode, and you hypothesize that the mood will change in a complex, non-linear fashion. Such an analysis could be facilitated using time-varying effect modeling (TVEM), which will allow you to model complex, non-linear trajectories of change in mood throughout the duration of a drinking episode.
These are but two of many possible examples for how ILD can be analyzed. The analysis of ILD is a burgeoning research area – new techniques for answering more nuanced questions about dynamic constructs and their interrelations are continually coming online, and Methodology Center investigators will remain actively engaged in this exciting research area.