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ASSIST-DailyLife Resource Core

Learn about ASSIST-DailyLife's structure, goals, services, and leadership that make up our Resource Core.

Structure

The ASSIST-DailyLife (Assessment & Intervention Science & Technology in Daily Life) Resource Core includes 3 sub-cores:

  • Person-Centered Outcomes Assessment & Technology (PCOAT)
  • Accelerometer Measurement of Physical Activity, Sedentary Behavior and Sleep (AMPASS)
  • Behavioral Intervention Technologies (BIT)

Goals

Our Resource Core's goal is to:

  • Provide consultation to support design and implementation of platforms for multi-modal assessment and health interventions incorporating real-world:
    • self-report of health, symptoms, and life satisfaction, and performance-based function
    • accelerometer monitoring to assess physical activity, sedentary behavior, and sleep
    • mobile, web, tablet, and sensor-based applications that identify real-world behavioral markers

Services

The ASSIST-DailyLife (Assessment & Intervention Science & Technology in Daily Life) Resource Core includes 3 sub-cores:

  • Person-Centered Outcomes Assessment & Technology (PCOAT)
  • Accelerometer Measurement of Physical Activity, Sedentary Behavior and Sleep (AMPASS)
  • Behavioral Intervention Technologies (BIT)

 Accelerometer Measurement of Physical Activity, Sedentary Behavior and Sleep (AMPASS) Services

NU Physical Activity in Rheumatology Research Group (PARR) has been the forefront in providing expertise on designing and implementing state-of-the-science daily life assessment of physical activity, sedentary behavior and sleep to investigators interested in studying persons with or at risk for rheumatic and musculoskeletal conditions.  The AMPASS subcore includes PARR leader and members (Julia Lee, Dorothy Dunlop, Pamela Semanik, Jing Song) who bring expertise in the processing of accelerometer monitoring output, biostatistics, and health services research.
  • Provide expertise on designing and implementing tailored, state-of-the-science daily life assessment of physical activity, sedentary behavior and sleep
  • Selection of device to monitor physical activity, sedentary behavior and sleep
  • Set up and program monitors
  • Resources for training research assistants for device management (e.g., instructions)
  • Provide resources to transform raw accelerometer data into meaningful parameters
    • Guidance on software setup/use
      • Non-wear time algorithm
      • Minutes spent in sedentary behavior, light-, moderate-, and vigorous intensity activity
      • Sedentary parameters (e.g., total time, breaks, bout etc)
    • Guidance on analysis and interpretation of accelerometer output
    • Consultation on options for data collection
    • Draft text for inclusion of monitoring in a grant proposal
    • Guidance on feasibility study design

 Behavior Intervention Technologies (BIT) Services

Northwestern’s Center for Behavioral Technologies (CBITs) has been a leading center in the development of scientific methods to study behavioral intervention technologies from development to implementation and pursuing research to understand how technologies can support people’s mental and physical health, and wellness.  The BIT Core brings expertise in design, interventions, and personal sensing.  The BIT subcore includes CBITs members (Stephen Schueller, David Mohr) who bring expertise in design, evaluation, and implementation of behavioral intervention technologies, trial design, user-centered design methods, and evaluation.
  • Provide expertise on designing and evaluating behavioral intervention technologies
    • Development processes based on user-centered design including:
      • Usability testing
      • Needs assessments and task analysis
      • Structured ideation and prototyping
      • Participatory design and cooperative inquiry
      • Cognitive walkthroughs and “talk alouds”
    • Field testing and in-situ evaluations
    • Clinical trial design
    • Implementation science and implementation trial design
  • Provide resources on vendor selection for software development
    • Guidance on appropriate vendors for development tasks
  • Guidance on analysis and interpretation of sensor-based data output
  • Consultation on options for data collection (e.g., active vs. passive, ecological momentary assessment)
  • Draft text for inclusion of behavioral intervention technologies in grant proposals
  • Guidance on trial design

 Person-Centered Outcomes Assessment & Technology (PCOAT) Services

The PCOAT subcore includes experts in measurement science, particularly in self-report instruments (patient-reported outcomes or PROs).  PROs measure physical, mental, and social health, symptoms, well-being, and life satisfaction.  PCOAT scientists have particular expertise with PROMIS, Neuro-QoL, and NIH Toolbox measures.
  • Topics in patient-reported outcome research
    • Identifying outcomes of interest
    • Measurement selection for outcomes of interest including evaluation of psychometric properties and intended use
    • Evaluation of available PRO measures
    • Feasibility review for developing new self-report measures
    • Interpreting magnitude of change
    • Utilizing PROs in assessment of quality of healthcare
    • Utilizing PROs in pharma trials
    • Translation of measures
  • Measurement of performance tests of cognitive, motor, and sensory function
    • Applicability of NIH Toolbox performance tests of cognitive, motor, and sensory function
  • Strategies for integration of measures in research protocols and clinical practice
  • Consultation on options for data collection systems
  • Draft text for inclusion of measures in a grant proposal
  • Guidance on score interpretation
  • Identification of opportunities to advance measurement science
  • Resources for training in the administration of NIH Toolbox cognition, motor, and sensation performance tests
  • Evaluation of feasibility in capturing PROs within Northwestern Medicine’s Epic electronic health record

Example Projects Using the ASSIST-Daily Life Resource Core

Researchers seeking consultation with the ASSIST-Daily Life can utilize one, two, or all three sub-cores. Below are examples of projects that have integrated the expertise in all three sub-cores.

 Example Project 1: Motivational Interviewing and Physical Activity Change in Parkinson’s Disease

PI: Linda Erlich-Jones, Shirley Ryan AbilityLab

Summary

The purpose of this study is to test the efficacy of a 6-month telephone-based motivational interviewing intervention and a web-based application intervention to improve physical activity in participants with Parkinson's disease.

Methods

Participants will be randomized into one of four groups to examine two separate interventions. The groups are: motivational interviewing (a counseling/coaching style), a web-based application for participants to keep track of their physical activity, a combination of the motivational interviewing and the web-based application, and an educational program on various issues related to Parkinson's disease. The intervention will last 6 months with a follow-up appointment at 9 months. Participants will be asked to come to Galter Pavilion at Northwestern Memorial Hospital or Shirley Ryan AbilityLab a total of five times over the course of the nine months.

https://clinicaltrials.gov/ct2/show/NCT03329833

Role of Sub-Cores

BIT

BIT Core contributed to the iterative development of the web-based self-monitoring application including development of a high-quality prototype, integration of feedback from focus group participants on the prototype, a first minimally-viable prototype with functional data collection for use in usability testing, feedback on usability testing protocols, and revisions based on usability testing.

AMPASS

Monitor change in physical activity using data from an Actigraph GT3X activity monitor.

Wear for one week every quarter. Time spent doing physical activity is compared over time.

Person-Centered Outcomes Assessment & Technology (PCOAT) Sub-Core

The PCOAT sub-core provided consultation on the patient-reported outcome measures (PROs) used to assess the impact of the intervention. This includes review of psychometric properties of measures, recommendations for assessment frequency, and analysis of PRO data. Selected self-report measures included Neuro-QoL, a set of measures quantifying physical, mental, and social health in individuals with neurologic conditions. The PCOAT sub-core also has expertise in the assessment of physical function through performance tests (e.g., balance, gait speed) and can assist in evaluating measures appropriate for use with this patient population. Consultation on the data collection tool is also possible

 Example Project 2: Mobil-Wise: Mobile Phone Remote Coaching After Worksite Joint Adventure Exposure

PI: Pam Semanik, Rush University, Shirley AbilityLab

Summary

This pilot aims to develop and evaluate the feasibility and acceptability of using a remotely coached intervention (Mobil-Wise) to attain and sustain healthy physical activity behavior among Blue Cross Blue Shield of Illinois employees with or at high risk for knee osteoarthritis. This pilot will use data transmitted from an affordable accelerometer-based personal monitor (Fitbit Flex) via customized remote-coach interface to:

  1. Allow a remote coach to view and collect physical activity data generated by the personal monitor and
  2. Use that data to formulate and provide tailored behavioral support using motivational interviewing.

The overarching goal of this program is to develop and disseminate an affordable, efficient, efficacious physical activity intervention to large groups of employees at risk for knee OA.

Methods

Mobil-Wise is a 12-week pilot RCT to test technology that transmits data via mobile phone between an affordable accelerometer-based personal monitor (Fitbit Flex) and a customized remote-coach interface to: 1) allow a remote coach to view and collect objective PA data generated by the personal monitor, and 2) use that data to formulate and provide tailored behavioral phone support, using motivational interviewing. The Mobil-Wise intervention will use the media communication mode preferred by the participant (text, voice call, or video chat) to attain and sustain healthy physical activity behavior in Blue Cross Blue Shield of Illinois employees with knee symptoms who have participated in an in- person, intensive intervention currently in progress. Six month follow-up measures were collected.

https://clinicaltrials.gov/ct2/show/NCT02950090

Role of Sub-Cores

BIT

BITCore was consulted for development of the custom remote interface but ended up contributing to the development of the patient recruitment site that included mobile consenting and participant tracking and management.

AMPASS

Physical activity assessed by ActiGraph GT3X+ triaxial accelerometer, measured pre- and post-intervention. Primary outcomes are average daily accelerometer counts, minutes of non-sedentary activity, minutes of light activity, and minutes of moderate-light physical activity.

Person-Centered Outcomes Assessment & Technology (PCOAT) Sub-Core

The PCOAT sub-core provided consultation on self-report measures of physical, mental, and social health. This included evaluation of psychometric properties, respondent burden, and the features and constraints of available self-report data collection systems. The Patient-Reported Outcomes Measurement Information System (PROMIS) computer adaptive tests (CAT) were selected. Because of the requirements for CAT administration, Assessment Center was identified as a necessary data collection tool. Other web-based applications including Nutrition Quest were used in the study. Because seamless integration of these different systems was not feasible within the grant budget, a member of PCOAT worked with the study team to create a study protocol that navigated study staff and participants through each component. The investigator also guided the set-up of Assessment Center to streamline data collection and minimize user burden.

 Example Project 3: Fallnet: Understanding Real Life Falls in Amputees Using Mobile Phone Technology

PI: Arun Jayaraman

Shawen, N., Lonini, L., Mummidisetty, C. K., Shparii, I., Albert, M. V., Kording, K., & Jayaraman, A. (2017). Fall detection in individuals with lower limb amputations using mobile phones: machine learning enhances robustness for real-world applications. JMIR mHealth and uHealth, 5(10).

https://mhealth.jmir.org/2017/10/e151

Summary

This project aimed to evaluate whether passive data collected from smartphone sensors (e.g., accelerometer and gyroscope) could be used to detect participant falls among control (non-amputee) individuals and individuals with a lower limb amputation.

Using existing architectural, infrastructural CBITs Android Native Smartphone software, provide the means to trigger interactions remotely with researchers and clinicians and locally with participants for the purpose of detecting and reporting participant falls.

Methods

Participants performed four types of simulated falls: forward (trip), backward (slip), left, and right. Non-amputee control participants performed both indoor and outdoor falls, participants with amputation only fell indoors. Participants carried the phone in three different locations: in a pouch worn on the waist, in a pants pocket, or in their hand. Data was also collected from participants with amputations at home for at least 2 days with ability to label when falls occurred.

Role of Sub-Cores

BIT

BIT developed Purple Robot was used for this project. Purple Robot is an Android app that functions as a research platform for collecting data through hardware sensors on an Android mobile phone. Purple Robot facilitated data collection from accelerometer and gyroscope sensor of mobile phones. Data sampling rate was approximately 50 Hz.

AMPASS

AMPASS was not part of the Fallnet study team, but could provide consultation on designing and implementing tailored, state-of-the-science assessment of activity.

Person-Centered Outcomes Assessment & Technology (PCOAT) Sub-Core

PCOAT was not part of the Fallnet study team, but could provide consultation when measures and analytic plans are in development.  In particular, evaluation of baseline physical function and motor function may be appropriate (e.g., balance, locomotion). Self-reported health constructs that may be related to fall risk or be outcomes of falls could be considered. For example, the relationship between self-report of depressive symptoms, accelerometer data, and GPS data could be explored as psychomotor agitation and retardation are associated with low mood. Objective measures of cognitive function may also predict fall risk. PCOAT can collaborate with BIT to integrate self-report measures into the application as well as include administered tests results in the study database.

Leadership

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FIRST-DailyLife can help you through an array of consultative services originating from three Cores.

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Additional Resources

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