Name: methodology for establishing a community-wide life laboratory for capturing unobtrusive and continuous remote activity and health data
Uploaded: 2018-07-27T12:30:00
Description: Watch this Scientific Journal Video about Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data at JoVE.com

The research described here was supported by grants from the National Institutes of Health, National Institute on Aging (U2CAG054397, P30 AG024978, P30 AG008017, R01 AG042191, R01 AG024059), Intel, the Foundation for the National Institutes of Health and the Robert Wood Johnson Foundation.

All participants provided written informed consent. Life Laboratory participants are asked to live their lives as they normally would to allow longitudinal observational study of their life activities and patterns for the rest of their lives. They may withdraw at any time if they wish. The study protocol was approved by the Oregon Health &#38;amp; Science University (OHSU) Institutional Review Board (Life Laboratory OHSU IRB #2765).
1. Preparation
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	<li>Prior to deployment, add all equip...

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We have described a basic system or platform enabling home- and community-based remote sensing and reporting of salient health and wellbeing measures on a continuous basis. The system is intended to be used primarily in research at this time.
Where possible, the system uses open source tools and sensors or devices taking advantage of available APIs and software development kits (SDK). The system is designed to be technology &#34;agnostic&#34; such that a wide variety of sensors or devices can ...

Prevailing clinical research is fraught with limitations in the reliability and validity of data captured because of inherent shortcomings of assessment methodology. Interviews are constrained by the times when the clinician and patient can coordinate schedules. Time allotted for examinations is limited by what the volunteer can reasonably be asked to do in a single session. These brief, widely spaced sessions - even if augmented by occasional telephone calls or Internet queries - severely limit the potential to detect meaningful change in function or wellbeing over time. Current test sessions are largely composed of requests for information that can be difficult to recall and verify (e.g., &#34;do you remember to take your medications?&#34;) or performance of artificial tasks (e.g., &#34;stand up and sit down as fast as you can&#34;; &#34;remember these ten words&#34;). The assessments are often designed to restrict test-to-test variability when in fact variability in performance itself may be a key diagnostic feature. Further, these brief biopsies of time are conducted under artificial conditions rather than within the normal flow of day-to-day life. Therefore, they are of limited ecological validity. Finally, the current paradigm inherently cannot provide direct linkage of interdependent key events or outcomes (e.g., sleep, socialization, physical activity) because the data are not time-stamped other than as recalled.
An approach to overcoming these shortcomings lies in the development of systems that can be embedded in home or community that take advantage of advances in pervasive computing and sensing technology, wireless communications, and high frequency multi-domain data analytics. The technology and experience in this area are growing and a number of systems have been developed, but have been limited in deployment, features or longitudinal experience1,2,3,4. In this manuscript, we describe a protocol developed as a means to provide real-time, continuous and longitudinal home-based assessment of health-relevant data to improve upon the limitations of the current health assessment paradigm. Oregon Center for Aging &#38;amp; Technology (ORCATECH) has developed a home-based system based on pervasive computing and sensing technology to provide continuous, real-time assessment of health-relevant activity and behavior. Bringing the assessment into home to enable largely unobtrusive and continuous monitoring of real-world activity substantially overcomes current limitations. First, since the core system is embedded in the life-space of the participants as part of their ambient environment, it is inherently convenient. Assessments requiring discrete responses can be collected when a person is most at ease and, in the case of passive collection methods, as frequently as necessary without burdening a participant. Second, being in the person&#39;s normal life-space affords the opportunity to collect data that are immediately ecologically relevant, not simply testing contrived measures of function, but everyday cognition. For example, prospective memory failure, a common complaint difficult to naturalistically test in the clinic, can be assessed at home by automatic daily tracking of medication taking behavior, thus tapping both everyday cognition, as well as a key performance metric known to be sensitive to cognitive change. Third, because the data are digital and time-stamped, measurement of multiple interrelated measures aligned in time is facilitated. For example, time on the telephone and time out of home (measures of social engagement or withdrawal), computer use (measuring initiation, psychomotor activity and cognitive function), and other measures that have been shown to change with functional decline (sleep behavior, weight, walking speed) can add to the sensitivity of the sensor net to distinguish subtle changes that may not be otherwise apparent. Importantly, the effect of health and life events on cognition and function (e.g., weekly reports of pain, medication change, low mood) can also be linked to this data stream as they occur. Finally, conventional testing and queries can be presented via computer or related interfaces (e.g., tablet, smartphone), providing unparalleled opportunities to simultaneously compare legacy test performance to novel digitally derived measures from the same tests, such as response or pause times, learning curves and intra-test variability. This new approach thus transforms current assessment to be more convenient, unobtrusive, continuous, multi-domain and naturalistic. Ultimately, the basic platform of in-home sensor based assessment technology and methodology, provides a system that can be tuned and scaled to address a wide range of specific research questions related to health and wellbeing with noted advantages over the current accepted practice of infrequent clinic or telephone-based assessments.
The following protocol outlines the process of deploying this platform for unobtrusive in-home behavioral and health-related data collection. In developing this platform, a key goal has been to provide a basic suite of assessment functions that can provide the data necessary to infer both general domains of health and well-being (physical, cognitive, social, emotive), as well as more specific behaviors (e.g., medication taking, walking, sleep-related activities, physiologic activity). The development of the platform has been guided by several principles including using the most passive unobtrusive sensing approaches, minimizing direct user engagement with technology, being technology &#39;agnostic&#39; (i.e., employing the best devices or technical solutions rather than requiring a particular approach or product), being durable (for long-term assessment) and scalable, and minimizing hands-on maintenance.
The platform described has evolved over the past twelve years, importantly informed by a range of end-users, from &#34;digitally naive&#34; to early adopters. Periodic surveys and focus groups have been key to informing this development5,6,7. Hundreds of volunteers have allowed the systems to be continuously deployed in their homes for up to eleven years with iterative modifications being introduced based on the advances in technology, new functional capacities requested by the research community, and the key constant input of individuals living in home where the technology has been deployed. Collectively, these volunteers have formed a &#34;living&#34; laboratory in the community which we call the &#34;Life Laboratory&#34; where their homes and the continuous data collected throughout the day provide a unique level of details about health, activity, and the life course.
A basic platform of sensing technology forms the backbone of the overall system for capturing continuous home-based data. The elements of this platform are described subsequently. The core platform is modified (elements may be added or removed) based on the information obtained during the process of gathering user attitudes, and beliefs and outcome measures of interest for the study using the research platform. Because data communication protocols are standardized, the system has been designed to allow any device that follows these protocols to be incorporated into the network.
The basic platform described here is based on the use case of volunteers in the Life Laboratory (LL) who consent to have the platform deployed within their homes to collect naturalistic activity and behavior data of their normal life activities for many years (longest current continuous deployment = 11 years).
The hub computer and Ethernet/WiFi connection allow data collection from system devices and transmission back to secure servers at ORCATECH without participant interference. The hub computer is configured to the specific participant and home set-up upon system installation using a laptop or tablet and a control panel that connects to a centralized digital participant management system. Additional data collection devices (such as sensors, MedTracker, and scale) can be configured by communicating with the hub computer in the same way.
The ORCATECH Console and Remote Technology Management System is a custom digital technology and data management system called &#34;Console&#34; that allows participant home technology configuration and system set-up, as well as ongoing remote technology management of homes including secure data collection and monitoring. In addition, to facilitate deployment of the system in the community where each home may have a unique layout, a graphing tool based on a tablet interface is used to automatically record where various sensors are located and their valid physical adjacencies to other sensors (Figure 2). This is important for reference during remote monitoring of the system at the home level.
Passive infrared (PIR) Motion Sensors are digitally assigned to a given home during system installation, communicating with the hub computer via a wireless USB Dongle. One sensor is placed per room to sense motion within the room and participant transitions from room to room. A straight &#34;Sensor Line&#34; of four sensors is placed on the ceiling of a hallway or other area where the participant walks regularly at a consistent pace. This sensor line allows unobtrusive gathering of walking speed many times per day. Other metrics can be derived from these activity sensors such as dwell time or number of room transitions. Door contact sensors are placed around the home at all external doors to detect participants&#39; coming and going from the home, and on the refrigerator to determine general frequency of food access.
On-Line Weekly Health and Activity Self-Reports are required to make optimal sense of the data from the passive system of collection devices. These data are critical to the analysis of participant report of events in the home relative to the sensor collected data. The online weekly self-report survey can be completed on any computing device (e.g., laptop, tablet, smartphone) with an Internet connection to query participants about trips out of the home, visitors in the home, health changes, space changes within the home, loneliness, depression, and pain level. Weekly data collection relies on a relatively short window of recollection, which provides much higher resolution of data and likelihood of accuracy than, for instance, annual or semi-annual check-ups. Furthermore, this self-report process also allows investigators to examine passive indicators of potential cognitive impairment, such as variation in the time to complete the survey, variation in number of clicks, increased difficulties reporting accurate dates, or impairment markers in free text responses. As part of the basic platform, we install a seven day electronic pill box that records whether or not the designated day&#39;s compartment was opened and the time(s) that it was opened each day. This provides information about medication adherence as well as a potential indication of cognitive decline if consistency of medication-taking decreases.
A wireless digital bioimpedence scale that also collects pulse, body composition metrics, pulse wave velocity, environmental temperature, and ambient carbon dioxide level is installed in the bathroom, providing data on participants&#39; daily weight. This data can then be correlated with other reported events (e.g., health status, medications), as well as other passive indicators of behavior, such as protocol adherence and frequency of use over time.
In cases where our participants drive, we install a driving sensor in their vehicles. This sensor provides information about driving habits such as frequency, timing, duration, and distance of trips, as well as frequency of hard stops or hard accelerations.
A wrist worn wearable device collects physical activity data both in and out of the home. Several brands and models of wearables have been used in Life Laboratory homes.
Depending on the project, an investigator using the ORCATECH platform may choose to supplement the basic sensor set with additional data-collection components. Examples tested in the past include a phone sensor to monitor socialization through landline phone activity, the development and implementation of a digital balance-board for balance testing, a tablet with periodic cognitive tasks for the participant to complete in their own home, and an automated texting system to evaluate the efficacy of medication reminders via phone.
To handle the diverse data generated by the ORCATECH Life Lab, a tailored information and data system is used for collecting, annotating, maintaining, and analyzing copious activity and health data. ORCATECH has developed a custom system for participant management, self-report data collection and processing, and continuous data collection from all system devices and sensors. The system relies on a distributed NoSQL Cassandra server cluster to store the sensor data and a lambda architecture using Kafka and Spark which allows our data processing capabilities to move closer to real-time processing. Using a REST API, data is transferred into standard data analysis platforms and statistical software programs for data analysis.

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			<td height="42" style="height:42px;width:216px;">Raspberry Pi 3 Model B</td>
			<td style="width:123px;">Raspberry Pi Foundation</td>
			<td style="width:167px;">Raspberry Pi 3 Model B</td>
			<td style="width:167px;"></td>
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			<td height="21" style="height:21px;width:216px;">Motion Sensor</td>
			<td style="width:123px;">NYCE Sensors Inc</td>
			<td style="width:167px;">NCZ-3041-HA</td>
			<td></td>
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			<td height="21" style="height:21px;width:216px;">Door/Window Sensor</td>
			<td style="width:123px;">NYCE Sensors Inc</td>
			<td style="width:167px;">NCZ-3011-HA</td>
			<td></td>
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			<td height="21" style="height:21px;width:216px;">Curtain Motion Sensor</td>
			<td style="width:123px;">NYCE Sensors Inc</td>
			<td style="width:167px;">NCZ-3045-HA</td>
			<td></td>
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			<td height="21" style="height:21px;width:216px;">iSort</td>
			<td style="width:123px;">TimerCap</td>
			<td style="width:167px;">iSort</td>
			<td></td>
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			<td height="42" style="height:42px;width:216px;">Home Stealth USB Phone Recorder</td>
			<td style="width:123px;">Fiho</td>
			<td>Fi3001B</td>
			<td></td>
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			<td height="21" style="height:21px;width:216px;">Automatic Pro</td>
			<td style="width:123px;">Automatic</td>
			<td style="width:167px;">AUT-350C</td>
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			<td height="21" style="height:21px;width:216px;">Body Cardio Scale</td>
			<td style="width:123px;">Nokia</td>
			<td>WBS04</td>
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			<td height="21" style="height:21px;width:216px;">Activite/Steel Activity Monitor</td>
			<td style="width:123px;">Nokia</td>
			<td style="width:167px;">HWA01 STEEL</td>
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			<td height="21" style="height:21px;width:216px;">Alta 2</td>
			<td style="width:123px;">Fitbit</td>
			<td style="width:167px;">FB406</td>
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			<td height="21" style="height:21px;width:216px;">Charge 2</td>
			<td style="width:123px;">Fitbit</td>
			<td style="width:167px;">FB407</td>
			<td></td>
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			<td height="21" style="height:21px;width:216px;">Flex 2</td>
			<td style="width:123px;">Fitbit</td>
			<td style="width:167px;">FB403</td>
			<td></td>
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			<td height="21" style="height:21px;width:216px;">Zigbee USB Stick</td>
			<td style="width:123px;">Silicon Labs</td>
			<td style="width:167px;">ETRX3USB</td>
			<td></td>
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methodology for establishing a community-wide life laboratory for capturing unobtrusive and continuous remote activity and health data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

The ORCATECH suite of technology makes it possible to collect a uniquely rich data set about the life patterns of people as they go about their usual activities. The sensor system allows unobtrusive and continuous monitoring of the volunteers in their own homes. The system has been used in dozens of studies involving hundreds of volunteers in research examining key domains of health and function such as walking speed and mobility, medication-taking behavior, mood, time in or out of home, ...

Watch this Scientific Journal Video about Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data at JoVE.com

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

This methodology can provide real time, objective, continuous and ecologically valid data for researchers engaged in longitudinal health studies and clinical trials. The advantages of this approach are that it while it requires little to no effort on the part of the research participants, it enables the capture of acute data as well as subtle changing events over time. It is adaptable for multiple-use cases.This methodology has been substantially deployed to access outcomes most related to functional changes important for research in high-impact chronic conditions such as disorders of mobility or dementia. The protocols intend to be extensible in application of research to collect digital, activity and health data across many different research domains. Prior to deployment, add all the equipment to the console inventory system online.Assign a name to each device or sensor, along with its serial number and MAC address, allowing it to be stored within the console inventory system. Place a QR or quick response code tag on each piece of equipment enabling specific assignments of home location for the sensors and devices when deployed in the home. Before deployment, all sensors and devices that are to be installed will be assigned to the home by scanning the QR code on the sensor or device.This will bring up a website that allows sensor or device assignment to that specific home. Install the hub computer with an SD card which contains the hub configuration management tool. Pack all of the now inventoried sensors and equipment and hub computer with installed SD card into an installation kit for home deployment.Verify that the participant's home has an Internet service provider. Set up the hub computer by inserting the wireless dongle and the primary zigbee coordinator dongle into the hub computer. Insert the ethernet cable into the hub computer in order to connect to the home's Internet connection.And lastly, plug the hub computer's power cord into a centrally-located room's power socket. Connect and Internet-enabled device to the hub computer's wireless network. Open a web browser in order to access the local Control Panel website.The Control Panel website will show the status of the hub computer and once the installation is complete, any sensors installed in the home. Run the software configuration tool ensuring the appropriate software is installed. Do this by navigating to the Control Panel and running update.Navigate to the Control Panel to check that the hub computer is communicating with the main servers. Make sure that the services that allow data to be collected from each installed device and sensor are up and running. Add the sensors to the home, beginning with the motion sensors.Start by opening the Sensor Placement website from the Control Panel. In the Sensor Placement website create a virtual floor plan of the home by adding the areas that represent rooms in the home. Adding sensors.And creating linkages between areas and sensors. Add each motion sensor to the personal area network or PAN using a toggle switch located on the sensor page within the Control Panel. At the same time, press a button located near the motion sensor's battery 10 times.This two-part process activates the sensor. Using the virtual floor plan as a guide, place the motion sensors in their corresponding rooms in the home. The motion sensors should be placed on the walls at head height using tape.Continuing to use the virtual floor plan as a guide install a row of four restricted field sensors on the ceiling of a straight walkway such as a hallway in order to capture walking speed. Space these walking speed sensors two feet or 61 centimeters apart. Record the exact distance between the restricted field sensors in the Sensor Placement website.Install door sensors on each egress door again using the virtual floor plan to indicate their physical location. Add the pillbox to the pan by navigating to the devices column within the Control Panel. Turn on the device, because the pillbox connects to the hub computer.Ensure it is close enough to the hub computer for the signal to be detected. Set up the scale by navigating to the Control Panel opening the left navigation menu, choosing the expand device tab and clicking on the name of the scale. On the scale hold the side button for 10 seconds.The scale will turn on displaying a start up message. Once the scale appears on the Control Panel, click the setup gear button to start the setup process. Once the setup gear button turns green the scale will continue setting up.Place the scale in a location that has a flat, solid surface and is easily accessible to the participant. Enter the participant's height and weight when prompted in the Control Panel. If the participant does not have a pacemaker, toggle the pacemaker button in the Control Panel notifying the scale that it can collect certain data.Once the scale is set up, have the participant weigh themselves, confirming the scale is recording the initial weight that has been entered into the Control Panel. Set up the wrist-worn wearable device by navigating to the wearables setup page within the Control Panel and pressing the reset button located on the back side of the device 10 times. After the wearable device shows up in the list of wearable devices on the Control Panel, click the setup button in order to start the setup process.Once the wearables account is setup, calibrate the time by using the wheel on the setup page. To finalize the setup, synchronize the wearable by clicking the sync button in the Control Panel. This confirms that device is connecting properly and that the time is set to the same time as the hub computer.Different devices may require different procedures depending on the manufacturer. Additional sensors and devices may be integrated and deployed into the technology platform in the future. Once all the devices and sensors have been installed in the home confirm that the hub computer is working properly.Review if data is streaming from each device by navigating to the data collection page on the Control Panel. Walk near the sensors installed in each room of the home in order to see if each sensor is collecting data about recent movements. Check the motion sensors by viewing the live chart of motion sensor data.Check if the pillbox is transmitting data by opening and closing at least one of the compartment doors a few times. Review the data collection page on the Control Panel to see if this recent activity was measured and collected. In order to check if the scale is transmitting data, weigh yourself.Review if the scale is properly synced by navigating to the synced column within the devices page in the Control Panel. Check if the wearable device is properly synced and is transmitting data by navigating to the synced column within the wearables devices page in the Control Panel. The plot shows the trajectories of change in walking speed variability captured during multiple transits through home sensor lines.Variability was defined as the coefficient of variation in a cohort of older adults followed in their homes for over three years. Latent trajectory models identified four distinct groups of walking speed variability. Distinct patterns of change in walking variability were seen more commonly in early and later phases of mild cognitive impairment.This suggests that walking speed variability can be a marker for MCI progression. This spiral plot demonstrates the display of six months of continuous home activity data for a single volunteer. Data are plotted as a 24-hour clock with midnight at the top and noon at the bottom.Each day forms one circle. The solid blue circles mark one month boundaries. Colored points indicate sensor activity by room location where red is the bathroom, green is the bedroom, pink is the kitchen and blue is the living room.Consistent patterns of behavior are observed. Bedtime is usually around 2200 hours as visualized by sensor activity in the bedroom following sensor activity in the bathroom. The participants are up once most nights between 2 a.m.and 4 a.m. as evidenced by sensor activity in the bedroom and bathroom during that time. Rise time is 0600 hours as observed through sudden sensor activity at that time.Also apparent is a visit from a housekeeper at 5 p.m. every two weeks evidenced by the increased movement in the kitchen, followed by other rooms during that time. Finally, note the two week period during the fourth month when the volunteer did not leave the apartment during a community congregation restriction due to a flu epidemic.Once mastered, this technique can be utilized in less than 90 minutes, depending on the size of the home and the number of devices or senors utilized. The system is designed to allow ongoing or future studies to update or deploy new sensors or devices such as other wearables, bed activity sensing systems or home environmental condition sensors. The system has been deployed to hundreds of homes and continues to evolve as technologies advance.After watching this video, you should have a good understanding about how to deploy and integrate into your research pervasive computing technologies and sensors that will provide a more real time, continuous and ecologically valid set of data for your research.

methodology-for-establishing-community-wide-life-laboratory-for

Research

JoVE Journal

Behavior

Nest Building as an Indicator of Health and Welfare in Laboratory Mice

The minimization and alleviation of suffering has moral and scientific implications. In order to mitigate this negative experience one must be able to identify when an animal is actually in distress. Pain, illness, or distress cannot be managed if unrecognized. Evaluation of pain or illness typically involves the measurement of physiologic and behavioral indicators which are either invasive or not suitable for large scale assessment. The observation of nesting behavior shows promise as the basis of a species appropriate cage-side assessment tool for recognizing distress in mice. Here we demonstrate the utility of nest building behavior in laboratory mice as an ethologically relevant indicator of welfare. The methods presented can be successfully used to identify thermal stressors, aggressive cages, sickness, and pain. Observation of nest building behavior in mouse colonies provides a refinement to health and well-being assessment on a day to day basis.

We demonstrate the utility of nest building behavior in laboratory mice as an indicator of welfare. Nest scoring is a sensitive technique that is altered by temperature, illness, and aggression. The time to integrate into nest test (TINT) is a simple cage-side assessment that can detect postoperative pain.

The minimization and alleviation of  suffering has moral and scientific implications. In order to mitigate this  negative experience one must be able to ...

Barnes Maze Testing Strategies with Small and Large Rodent Models

Spatial learning and memory of laboratory rodents is often assessed via navigational ability in mazes, most popular of which are the water and dry-land (Barnes) mazes. Improved performance over sessions or trials is thought to reflect learning and memory of the escape cage/platform location. Considered less stressful than water mazes, the Barnes maze is a relatively simple design of a circular platform top with several holes equally spaced around the perimeter edge. All but one of the holes are false-bottomed or blind-ending, while one leads to an escape cage. Mildly aversive stimuli (e.g.&#160;bright overhead lights) provide motivation to locate the escape cage. Latency to locate the escape cage can be measured during the session; however, additional endpoints typically require video recording. From those video recordings, use of automated tracking software can generate a variety of endpoints that are similar to those produced in water mazes (e.g.&#160;distance traveled, velocity/speed, time spent in the correct quadrant, time spent moving/resting, and confirmation of latency). Type of search strategy (i.e.&#160;random, serial, or direct) can be categorized as well. Barnes maze construction and testing methodologies can differ for small rodents, such as mice, and large rodents, such as rats. For example, while extra-maze cues are effective for rats, smaller wild rodents may require intra-maze cues with a visual barrier around the maze. Appropriate stimuli must be identified which motivate the rodent to locate the escape cage. Both Barnes and water mazes can be time consuming as 4-7 test trials are typically required to detect improved learning and memory performance (e.g.&#160;shorter latencies or path lengths to locate the escape platform or cage) and/or differences between experimental groups. Even so, the Barnes maze is a widely employed behavioral assessment measuring spatial navigational abilities and their potential disruption by genetic, neurobehavioral manipulations, or drug/ toxicant exposure.

The dry-land Barnes maze is widely used to measure spatial navigation ability in response to mildly aversive stimuli. Over consecutive days, performance (e.g.&#160;latency to locate escape cage) of control subjects improves, indicative of normal learning and memory. Differences between rats and mice necessitate apparatus and methodology changes that are detailed here.

Spatial learning and memory of laboratory rodents is often assessed via navigational ability in mazes, most popular of which are the water and dry-land ...

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

A protocol for capturing and statistically analyzing emotional response of a population to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis software is described.

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

The Treadmill Fatigue Test: A Simple, High-throughput Assay of Fatigue-like Behavior for the Mouse

Fatigue is a prominent symptom in many diseases and disorders and reduces quality of life for many people. The lack of clear pathogenesis and failure of current interventions to adequately treat fatigue in all patients leaves a need for new treatment options. Despite the therapeutic need and importance of preclinical research in helping identify promising novel treatments, few preclinical assays of fatigue are available. Moreover, the most common preclinical assay used to assess fatigue-like behavior, voluntary wheel running, is not suitable for use with some strains of mice, may not be sensitive to drugs that reduce fatigue, and has relatively low throughput. The current protocol describes a novel, non-voluntary preclinical assay of fatigue-like behavior, the treadmill fatigue test, and provides evidence of its efficacy in detecting fatigue-like behavior in mice treated with a chemotherapy drug known to cause fatigue in humans and fatigue-like behavior in animals. This assay may be a beneficial alternative to wheel running, as fatigue-like behavior and potential interventions can be assessed in a greater number of mice over a shorter time frame, thus permitting faster discovery of new therapeutic options.

Fatigue is a common, undertreated and frequently poorly-understood symptom in many diseases and disorders. New preclinical assays of fatigue may help to improve current understanding and future treatment of fatigue. To that end, the current protocol provides a novel means of measuring fatigue-like behavior in the mouse.

Fatigue is a prominent symptom in many diseases and disorders and reduces quality of life for many people. The lack of clear pathogenesis and failure of ...

Introducing Clicker Training as a Cognitive Enrichment for Laboratory Mice

Establishing new refinement strategies in laboratory animal science is a central goal in fulfilling the requirements of Directive 2010/63/EU. Previous research determined a profound impact of gentle handling protocols on the well-being of laboratory mice. By introducing clicker training to the keeping of mice, not only do we promote the amicable treatment of mice, but we also enable them to experience cognitive enrichment. Clicker training is a form of positive reinforcement training using a conditioned secondary reinforcer, the "click" sound of a clicker, which serves as a time bridge between the strengthened behavior and an upcoming reward. The effective implementation of the clicker training protocol with a cohort of 12 BALB/c inbred mice of each sex proved to be uncomplicated. The mice learned rather quickly when challenged with tasks of the clicker training protocol, and almost all trained mice overcame the challenges they were given (100% of female mice and 83% of male mice). This study has identified that clicker training for mice strongly correlates with reduced fear in the mice during human-mice interactions, as shown by reduced anxiety-related behaviors (e.g., defecation, vocalization, and urination) and fewer depression-like behaviors (e.g., floating). By developing a reliable protocol that can be easily integrated into the daily routine of the keeping of laboratory mice, the lifetime experience of welfare in the mice can be improved substantially.

The development of new refinement strategies for laboratory mice is a challenging task that contributes towards fulfilling the 3R principle. This protocol introduces clicker training as a cognitive enrichment program for laboratory mice.

Establishing new refinement strategies in laboratory animal science is a central goal in fulfilling the requirements of Directive 2010/63/EU. Previous ...

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Our knowledge of human object vision is based almost exclusively on studies in which the stimuli are presented in the form of computerized two-dimensional (2-D) images. In everyday life, however, humans interact predominantly with real-world solid objects, not images. Currently, we know very little about whether images of objects trigger similar behavioral or neural processes as do real-world exemplars. Here, we present methods for bringing the real-world into the laboratory. We detail methods for presenting rich, ecologically-valid real-world stimuli under tightly-controlled viewing conditions. We describe how to match closely the visual appearance of real objects and their images, as well as novel apparatus and protocols that can be used to present real objects and computerized images on successively interleaved trials. We use a decision-making paradigm as a case example in which we compare willingness-to-pay (WTP) for real snack foods versus 2-D images of the same items. We show that WTP increases by 6.6% for food items displayed as real objects versus high-resolution 2-D colored&#160;images of the same foods -suggesting that real foods are perceived as being more valuable than their images. Although presenting real object stimuli under controlled conditions presents several practical challenges for the experimenter, this approach will fundamentally expand our understanding of the cognitive and neural processes that underlie naturalistic vision.

We describe methods for presenting real-world objects and matched images of the same objects under tightly-controlled experimental conditions. The methods are described in the context of a decision-making task, but the same real-world approach can be extended to other cognitive domains such as perception, attention, and memory.

Our knowledge of human object vision is based almost exclusively on studies in which the stimuli are presented in the form of computerized two-dimensional ...

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Using multimodal sensors is a promising way to understand the role of social interactions in educational settings. This paper describes a methodology for capturing joint visual attention from colocated dyads using mobile eye-trackers.

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency ...

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Recent studies have shown that an automated, lifespan-inclusive, transdiagnostic, and clinically based, individualized risk calculator provides a powerful system for supporting the early detection of individuals at-risk of psychosis at a large scale, by leveraging electronic health records (EHRs). This risk calculator has been externally validated twice and is undergoing feasibility testing for clinical implementation. Integration of this risk calculator in clinical routine should be facilitated by prospective feasibility studies, which are required to address pragmatic challenges, such as missing data, and the usability of this risk calculator in a real-world and routine clinical setting. Here, we present an approach for a prospective implementation of a real-time psychosis risk detection and alerting service in a real-world EHR system. This method leverages the CogStack platform, which is an open-source, lightweight, and distributed information retrieval and text extraction system. The CogStack platform incorporates a set of services that allow for full-text search of clinical data, lifespan-inclusive, real-time calculation of psychosis risk, early risk-alerting to clinicians, and the visual monitoring of patients over time. Our method includes: 1) ingestion and synchronization of data from multiple sources into the CogStack platform, 2) implementation of a risk calculator, whose algorithm was previously developed and validated, for timely computation of a patient&#39;s risk of psychosis, 3) creation of interactive visualizations and dashboards to monitor patients&#39; health status over time, and 4) building automated alerting systems to ensure that clinicians are notified of patients at-risk, so that appropriate actions can be pursued. This is the first ever study that has developed and implemented a similar detection and alerting system in clinical routine for early detection of psychosis.

We demonstrate how to deploy a real-time psychosis risk calculation and alerting system based on CogStack, an information retrieval and extraction platform for electronic health records.

Recent studies have shown that an automated, lifespan-inclusive, transdiagnostic, and clinically based, individualized risk calculator provides a powerful ...

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

"Implementation" of new initiatives in healthcare settings typically encompasses two distinct components: a "clinical intervention" plus accompanying "implementation strategies" that support putting the clinical intervention into day-to-day practice. A novel clinical intervention, for example, might consist of a new medication, a new protocol, a new device, or a new program. As clinical interventions are not self-implementing, however, they nearly always require effective implementation strategies in order to succeed. Implementation strategies set out to engage healthcare providers, staff and patients in ways that increase the likelihood of the new initiative being successfully adopted, a process that often involves behavior change and new ways of thinking by participants. One of the challenges in studying implementation is that it can be difficult to collect data about the status and progress of implementation, including participants' own perspectives and experiences concerning implementation to date. This protocol describes a novel method for collecting and analyzing data related to ongoing implementation called the Participant-Reported Implementation Update and Score, or PRIUS. The PRIUS method allows for the efficient and systematic capture of qualitative and quantitative data that can provide a detailed and nuanced account of implementation over time and from multiple viewpoints. This longitudinal method can enable researchers, as well as implementation leaders and organizational stakeholders, to monitor implementation progress more closely, conduct formative evaluation, identify improvement opportunities, and gauge the effect of any implementation changes on a rolling basis.

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

This protocol describes a novel method for collecting and analyzing data related to ongoing implementation called the Participant-Reported Implementation Update and Score (PRIUS). The PRIUS method allows for the efficient and systematic capture of data over time and from multiple viewpoints in healthcare settings.

"Implementation" of new initiatives in healthcare settings typically encompasses two distinct components: a "clinical intervention" plus accompanying ...

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous technological changes and scientific advances, there is a need for life-long learning and education within both formal and non-formal educational environments. In response to this challenge, the use of eye-tracking technology and data-mining techniques, respectively, for supervised (mainly prediction) and unsupervised (specifically cluster analysis) learning, provide methods for the detection of forms of learning among users and/or the classification of their learning styles. In this study, a protocol is proposed for the study of learning styles among adults with and without previous knowledge at different ages (18 to 69-year-old) and at different points throughout the learning process (start and end). Statistical analysis-of-variance techniques mean that differences may be detected between the participants by type of learner and previous knowledge of the task. Likewise, the use of&#160;unsupervised learning clustering techniques throws light on similar forms of learning among the participants across different groups. All these data will facilitate personalized proposals from the teacher for the presentation of each task at different points in the chain of information processing. It will likewise be easier for the teacher to adapt teaching materials to the learning needs of each student or group of students with similar characteristics.

We present a protocol for a behavioral analysis of adults (ages 18 to 70-year-old) engaged in learning processes, undertaking tasks designed for Self-Regulated Learning (SRL). The participants, university teachers and students, and adults from the University of Experience, were monitored with eye-tracking devices and the data were analyzed with data-mining techniques.