When multiple measures are taken over intervals of time, change in those measures can be assessed. For example, pain intensity measures assessed before, during and after an intervention can be examined for change. However, when multiple measures are not available, researchers instead rely on patients to supply information on change by asking, for example, "how much has your pain improved since you began the treatment last month?"
The problem is that past experience is not recorded in memory as images are recorded in a video. People use a host of mental shortcuts (heuristics) to reconstruct the past. One of these heuristics is to use current experience as a guide to the past. If there is no reason to believe that the past should be any different from the now then the now becomes the past and that's what is reported. On the other hand, if there is some reason to believe the past my be different from the past then reports will be adjusted accordingly. For example, if people undergo a treatment for pain, their expectation of improvement will lead people to assume that their current pain should be less than their past pain. That is, pain should be improved after treatment compared to before treatment. According to Schwartz (2010), "asking patients whether they feel better now than before their treatment is the most efficient way to "improve" the success rate of medical interventions."
Monday, December 19, 2011
Saturday, December 17, 2011
The problem with retrospective reports: intensity
The problem with asking people about the intensity (How painful? How much anxiety? How optimistic? etc.) of a past experience is that intensity is not very well represented in memory. Therefore, when asked about intensity, people must use various mental tricks to reconstruct an answer. The problem is that these reconstructions of past experience bear little resemblance to the past experience as it was experienced.
In a frequently cited study, Redelmeier and Kahneman (1996) asked patients undergoing either colonoscopy or lithotripsy to rate their pain every 60 sec by moving a marker on a computer screen to indicate pain intensity. Then while recovering, within one hour of their procedure, patients were asked to assess their "total amount of pain experienced". Attending physicians were also asked to judge the overall pain experience of each patient. The researchers found that patients' retrospective reports of overall pain experienced were strongly correlated with both peak pain (.64) and end pain (.44) during the final 3 mins of the procedure. They also found that longer procedures were not predictive of recalling greater amount of pain. Even though a longer procedure subjects people to more pain overall, people don't seem to take account of the entire experience in their retrospective reports. Instead, people tend to recall only how bad it got (peak), and how it ended (end).
In addition to the peak/end heuristic, people also resort to various inference strategies. For example, asked about last week's pain, patients often try to contract an answer by noting how much pain they currently have, and then consider whether last week's pain might have been different. If so, the pain report will reflect this anomaly (Ross & Conway, 1986, Ross, 1989; Linton & Melin, 1982), otherwise the current pain will assumed to be representative of last week's pain (Eich et al, 1985) and that's what will be reported. As Schwartz (2010) put it, "... her retrospective report of pain is a function of her current pain and her naive theory about the stability of her pain over time."
In a frequently cited study, Redelmeier and Kahneman (1996) asked patients undergoing either colonoscopy or lithotripsy to rate their pain every 60 sec by moving a marker on a computer screen to indicate pain intensity. Then while recovering, within one hour of their procedure, patients were asked to assess their "total amount of pain experienced". Attending physicians were also asked to judge the overall pain experience of each patient. The researchers found that patients' retrospective reports of overall pain experienced were strongly correlated with both peak pain (.64) and end pain (.44) during the final 3 mins of the procedure. They also found that longer procedures were not predictive of recalling greater amount of pain. Even though a longer procedure subjects people to more pain overall, people don't seem to take account of the entire experience in their retrospective reports. Instead, people tend to recall only how bad it got (peak), and how it ended (end).
In addition to the peak/end heuristic, people also resort to various inference strategies. For example, asked about last week's pain, patients often try to contract an answer by noting how much pain they currently have, and then consider whether last week's pain might have been different. If so, the pain report will reflect this anomaly (Ross & Conway, 1986, Ross, 1989; Linton & Melin, 1982), otherwise the current pain will assumed to be representative of last week's pain (Eich et al, 1985) and that's what will be reported. As Schwartz (2010) put it, "... her retrospective report of pain is a function of her current pain and her naive theory about the stability of her pain over time."
Friday, December 16, 2011
What is ecological momentary assessment (EMA)?
According to Arthur Stone, who is one of the most prolific researchers and authors of EMA methodology, EMA refers to the "repeated collection of real-time data on participants' momentary states in the natural environment," and that "the key elements of EMA are real-time collection of data about momentary states, collected in the natural environment, with multiple repeated assessments over time." (Stone, Shiffman, Atienza, Nebeling, 2010). Let's briefly look at each of these elements.
Real-time data collection: Data are captured about individuals as life happens.
This means that instead of asking people how they felt or what they did in the past (which is much more frequently the case in health research), we capture how they feel as they're feeling it, and what they're doing, as they're doing it.
If a doctor asks you how much pain you've been having over the past month, you will likely try to recall how high your pain got and what proportion of the time your pain was at this level. This process puts a great burden on memory, which a tremendous volume of research has shown is fallible and subject to numerous biases that can shift the reality that we remember quite far from the reality that actually occurred.
EMA avoids this reliance on memory altogether by capturing data about mental and physical states at the time those states are occurring.
Momentary states: What's happening in and to individuals at brief slices of time (i.e., moments)
Imagine an assembly line making computers. To ensure quality, a robot is programmed to pluck individual computers from the line at random intervals and runs tests on them. You can think of each computer as a moment and the results of the tests as the data regarding the state of the system (the assembly line). That is data are collected on the behavior of the system by measuring the state of the computers at moments in time. Notice that not every computer (i.e., moment) is tested. Sure, testing every computer would provide an extremely detailed picture of the system but it would also pose a very heavy burden to do so. Therefore computers (moments) are sampled from among all possible computers (moments).
In the same way, EMA samples moments in peoples' lives. The data that are collected -- the variables -- depends on the research questions. Participants can be asked to rate how they currently feel on various emotions (happy, excited, frustrated, etc.), about the location and intensity of their physical pain, and their current level of physical capability on some scale, say, from 0 (not at all) to 10 (very much). Now, increasingly capable yet inexpensive devices are appearing that enable the measurement of a host of physiological parameters such as heart rate, skin conductance (for sympathetic arousal), heart rate variability, blood pressure, electromyography, and more. Things get particularly exciting when the relationship between self-report measures are correlated against objective physiological measures (but that's a topic beyond the scope of this article).
Natural environment: Data capture occurs as people go about their lives.
This is where the "ecological" in EMA comes in. Instead of bringing people into a lab, doing something to them, and then measuring their responses, we follow people out "in the wild" in their "natural habitats", where all the complexities of life and all the forces acting on people are preserved.
As an example, for my doctoral dissertation I investigated the effects of social disconnectedness on physical pain. In one of my studies, healthy undergrads were invited into the lab where they were exposed to a social interaction with a partner who was in fact a collaborator of the researchers posing as another participant. For some participants (the lucky ones), the partner was very warm and friendly, whereas for other participants, the partner was cool and aloof. Physical pain sensitivity was measured both before and after this social exchange. Everything was scripted and tightly controlled. But the problem is that uses contrived experiences in a relatively safe setting (a university lab) that are isolated from all the wonderful complexity of life. And this should make us wonder whether the results we obtain in such experiments apply beyond the walls of the lab, which is really the place where we hope the results apply because after all, life doesn't happen in a lab, it happens "out there". EMA techniques and technologies allow us to get "out there".
Repeated assessments over time: Data are captured multiple times daily over many days.
In most experimental studies, measurements are taken once. Participants roll in, measures are taken, then participants roll back out. In pre-post studies, measures are taken twice. In follow-up studies, measures may be taken three or four times. These studies are typically referred to, collectively, as repeated measures (RM) studies. EMA should not be confused with RM studies. EMA studies typically involve multiple assessments per day, repeated over a number of days. This dense sampling provides a level of temporal resolution that permits the examination of how dynamic processes unfold over time. For example, we can look at whether certain physiological parameters (arousal) immediately precedes anxious thoughts, or whether increasing levels of pain vs. steadily high pain levels lead to differences in psychological wellbeing.
Real-time data collection: Data are captured about individuals as life happens.
This means that instead of asking people how they felt or what they did in the past (which is much more frequently the case in health research), we capture how they feel as they're feeling it, and what they're doing, as they're doing it.
If a doctor asks you how much pain you've been having over the past month, you will likely try to recall how high your pain got and what proportion of the time your pain was at this level. This process puts a great burden on memory, which a tremendous volume of research has shown is fallible and subject to numerous biases that can shift the reality that we remember quite far from the reality that actually occurred.
EMA avoids this reliance on memory altogether by capturing data about mental and physical states at the time those states are occurring.
Momentary states: What's happening in and to individuals at brief slices of time (i.e., moments)
Imagine an assembly line making computers. To ensure quality, a robot is programmed to pluck individual computers from the line at random intervals and runs tests on them. You can think of each computer as a moment and the results of the tests as the data regarding the state of the system (the assembly line). That is data are collected on the behavior of the system by measuring the state of the computers at moments in time. Notice that not every computer (i.e., moment) is tested. Sure, testing every computer would provide an extremely detailed picture of the system but it would also pose a very heavy burden to do so. Therefore computers (moments) are sampled from among all possible computers (moments).
In the same way, EMA samples moments in peoples' lives. The data that are collected -- the variables -- depends on the research questions. Participants can be asked to rate how they currently feel on various emotions (happy, excited, frustrated, etc.), about the location and intensity of their physical pain, and their current level of physical capability on some scale, say, from 0 (not at all) to 10 (very much). Now, increasingly capable yet inexpensive devices are appearing that enable the measurement of a host of physiological parameters such as heart rate, skin conductance (for sympathetic arousal), heart rate variability, blood pressure, electromyography, and more. Things get particularly exciting when the relationship between self-report measures are correlated against objective physiological measures (but that's a topic beyond the scope of this article).
Natural environment: Data capture occurs as people go about their lives.
This is where the "ecological" in EMA comes in. Instead of bringing people into a lab, doing something to them, and then measuring their responses, we follow people out "in the wild" in their "natural habitats", where all the complexities of life and all the forces acting on people are preserved.
As an example, for my doctoral dissertation I investigated the effects of social disconnectedness on physical pain. In one of my studies, healthy undergrads were invited into the lab where they were exposed to a social interaction with a partner who was in fact a collaborator of the researchers posing as another participant. For some participants (the lucky ones), the partner was very warm and friendly, whereas for other participants, the partner was cool and aloof. Physical pain sensitivity was measured both before and after this social exchange. Everything was scripted and tightly controlled. But the problem is that uses contrived experiences in a relatively safe setting (a university lab) that are isolated from all the wonderful complexity of life. And this should make us wonder whether the results we obtain in such experiments apply beyond the walls of the lab, which is really the place where we hope the results apply because after all, life doesn't happen in a lab, it happens "out there". EMA techniques and technologies allow us to get "out there".
Repeated assessments over time: Data are captured multiple times daily over many days.
In most experimental studies, measurements are taken once. Participants roll in, measures are taken, then participants roll back out. In pre-post studies, measures are taken twice. In follow-up studies, measures may be taken three or four times. These studies are typically referred to, collectively, as repeated measures (RM) studies. EMA should not be confused with RM studies. EMA studies typically involve multiple assessments per day, repeated over a number of days. This dense sampling provides a level of temporal resolution that permits the examination of how dynamic processes unfold over time. For example, we can look at whether certain physiological parameters (arousal) immediately precedes anxious thoughts, or whether increasing levels of pain vs. steadily high pain levels lead to differences in psychological wellbeing.
Scosche myTrek: http://www.scosche.com/mytrek
SenseWear device can measure activity, energy expenditure and sleep
Worn on an armband, the BodyMedia SenseWear device contains a number of sensors: accelerometer to measure activity levels and steps taken, galvanic skin response (the only highly mobile, relatively inexpensive, device that I've seen that does this), skin temperature, and "heat flux" (amount of heat dissipating from the body). It can hold 28 days' worth of data and the raw data can be downloaded in Excel format. So it can be used to measure energy expenditure, step count, time spent in varying levels of activity (sedentary, moderate, vigorous), and sleep parameters (time lying down, sleep duration, sleep efficiency).They also have analysis and visualization software. Each device is $500 but a sales rep told me they offer quantity discounts: for quantities of 10-19 armbands, you get a 10% discount off list. If you order > 20, then you receive a 20% discount and the next discount applies for quantities of 50 or more. A bit expensive but it measures a lot. This product is promoted to clinicians and so is probably reliable; plus it has a great number of professional abilities and tools (e.g., you can configure channels for higher or lower sampling rates, etc.).
BodyMedia SenseWear: http://sensewear.bodymedia.com
AliveCor iPhone based ECG
This device (http://alivecor.com) looks like any case that you'd snap onto an iPhone, but it's got 2 stainless steel sensor plates on the back. To capture ECG you just put his/her left and right thumbs on the left and right plates (or place the whole device on the chest) and you get an instant clean ECG stream.
Check out http://alivecor.com/video.htm for demos from the inventor.
It's still awaiting FDA clearance.
It's still awaiting FDA clearance.
Shimmer wireless sensor system
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| Cardio Development Kit |
The cool thing about this system is that it is completely configurable and programmable. You can capture whatever phys data you want. They provide the wireless sensors and programming tools but you decide how you want to use them. You get a programmer who can configure the devices to capture exactly the data you need, at whatever schedule you want, and then do what you want to the raw data (could be just formatting it in a text file or Excel file format). They also have a LabView module.
- Phys applications: http://www.shimmer-research.com/applications-2/biophysical
- Kinematic applications: http://www.shimmer-research.com/applications-2/kinematics
They have a number of starter kits that they sell.
Most of my research has been in the area of pain and the kinematics stuff is really cool for pain apps because it enables the assessment of objective behavioral measures of treatment effectiveness. For example, you could detect actual degree/range of movement (using gyro to detect angle, location in space).
Wednesday, December 14, 2011
Striiv Device Aims to Make Fitness Fun Through Feedback
Striiv is a small device that tracks your steps. But any pedometer can do that. What makes Striiv interesting is what it does with the information it captures. Steps are turned into points to unlock achievements, that you can use to build things, grow plants and create buildings. And the more you create, the more coins you earn to build more things. It also has a very intriguing feature that enables you to turn steps into donations. The company Striiv has teamed with charities and donors so that walking counts towards donations that are made on your behalf (for no cost to you -- just walking). Check it out at http://www.striiv.com/
Study predicts illness with cellphone usage records
I recently came across a paper called "Social Sensing for Epidemiological Behavior Change". It describes a study in which smartphone use of college students was used to identify behavior changes associated with the presence of illness (colds, flu, fever, stress, depression).
The researchers gave smartphones to college students in a dormitory and tracked phone usage with call data records, sms logs, proximity sensing, and location-based sensing. Students also completed a brief questionnaire regarding the presence of symptoms. Proximity sensing enabled detecting when these student participants were physically close to other participants. The researchers found that student who developed a fever or cold tended to move around less and made fewer calls in the morning and late at night.
I think this is absolutely fascinating. It makes me think that there may be a whole world of meaning that can be extracted from data that are readily available but that we're missing. I rarely hear anyone in my field, Psychology/Neuroscience, talk about pattern recognition technologies -- we're trained to formulate hypotheses (expectations based upon what we know). But this is limiting. There's a world of surprises awaiting us if we just know how to look.
Track sleep unobtrusively with under-the-mattress sensor
A new device developed by BAM Labs, uses an inflatable pad that is placed under a mattress to unobtrusively track heart rate, respiration, motion and presence. Then data collected are wirelessly transmitted to a cloud-based service on the Internet, which can then be accessed through a website or with mobile devices.
This looks like a great tool for researchers. The really nice thing is that it tracks sleep parameters without requiring that individuals wear or attach anything to the body making it more acceptable to research subjects. Also, researchers (and caregivers) can monitor and collect data from many individuals remotely.
Unfortunately, it isn't yet available nor have they published release dates or prices.
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