Purdue Momentary Assessment Tool

Jul 13, 2013 The Purdue Pegboard Test is a rectangular board with 2 sets of 25 holes running vertically and 4 concave cups at the top. Small metal pegs are placed in the cup on the side being tested, with subjects asked to remove the pegs and place them vertically in the holes as rapidly as possible. The number of pegs placed in 30 seconds is scored. Constructing EMA studies with PMAT: The Purdue Momentary Assessment Tool user’s manual. West Lafayette, IN: Military Family Research Institute, Purdue University.

  1. Purdue Momentary Assessment Tool Download
  2. Purdue Momentary Assessment Tool Set
  3. Purdue Momentary Assessment Tool Set

The experience sampling method, also referred to as a daily diary method, or ecological momentary assessment (EMA), is an intensive longitudinal research methodology that involves asking participants to report on their thoughts, feelings, behaviors, and/or environment on multiple occasions over time.[1] Participants report on their thoughts, feelings, behaviors, and/or environment in the moment (right then, not later; right there, not elsewhere) or shortly thereafter.[2] Participants can be given a journal with many identical pages. Each page can have a psychometric scale, open-ended questions, or anything else used to assess their condition in that place and time. ESM studies can also operate fully automatized on portable electronic devices or via the internet.[3] The experience sampling method was developed by Larson and Csikszentmihalyi.[4]

Overview[edit]

There are different ways to signal participants when to take notes in their journal or complete a questionnaire,[5] like using preprogrammed stopwatches. An observer can have an identically programmed stopwatch, so the observer can record specific events as the participants are recording their feelings or other behaviors. It is best to avoid letting subjects know in advance when they will record their feelings, so they can't anticipate the event, and will just be 'acting naturally' when they stop and take notes on their current condition. Conversely, some statistical techniques require roughly equidistant time intervals, which has the limitation that assessments can be anticipated. Validity in these studies comes from repetition, so you can look for patterns, like participants reporting greater happiness right after meals. These correlations can then be tested by other means for cause and effect, such as vector autoregression,[6] since ESM just shows correlation.

Some authors also use the term experience sampling to encompass passive data derived from sources such as smartphones, wearable sensors, the Internet of Things, email and social media that do not require explicit input from participants.[7] These methods can be advantageous as they impose less demand on participants improving compliance and allowing data to be collected for much longer periods, are less likely to change the behaviour being studied and allow data to be sampled at much higher rates and with greater precision. Many research questions can benefit from both active and passive forms of experience sampling.

Software and related tools[edit]

The first mobile device application that could be used as a tool for Experience Sampling Method was the ESP Package (dating to the late 1990s). This had limited functionality in that it is designed for older iOS Palm devices and had limited scheduling capabilities. It no longer works on modern mobile devices.[8] iHabit was the first smartphone mobile application designed for Experience Sampling. It was developed in 2011 and used in a study published by PLOS One in 2013.[9] In 2015, it was superseded by the LifeData system, which was used in a study published by JAMA Pediatrics in 2016.[10] This system has subsequently been used in numerous studies. The PIEL Survey app (first version 2012) is a free app available in iOS[11] and Android [12]versions and has since been used in more than 12 academic publications. It can be used for scheduled, random and on-demand surveys. Unlike many platforms, no server is required as data is saved on the device and emailed to the researcher or else retrieved by file sharing.[13] Other early smartphone platforms for ESM include SurveySignal[14] and Ilumivu (developed in 2012), MetricWire (developed in 2013), Instant Survey, Movisens, and Aware (Open Source). The largest ESM study was achieved through PSYT's Mappiness App,[15] PSYT’s apps collect data through ESM as well as reporting the data back to users to enable real-time visualisation and tracking of variables. Several other commercial and open source systems are currently available to help researchers run ESM studies,[16] including BeepMe,[17] and Expimetrics.[18]Physiqual enables researchers to gather and integrate data from commercially available sensors and service providers to use them in ESM,[19] including Fitbit and Google Fit. As of 2014, Movisens have developed the ability to trigger sampling forms from physiological data such as actigraphy and ECG.[20]unforgettable.me provide a platform for both active and passive experience sampling that allows the integration of some 400 data sources.

In 2020, the AthenaCX platform (beta) was launched by an Irish based startup. AthenaCX's platform enables researchers to easily create and distribute experience sampling studies which can also be integrated with wearable devices; giving researchers access to health data along with their study. The powerful software enables researchers to trigger specific questionnaires which are dependent on a participant's gathered health/activity data. The platform has a central focus on the BYOD (Bring Your Own Device) process. The app is readily available from the Google and Apple App Stores so participants can get fully up and running within platform in a matter of minutes.

With context-sensitive experience sampling, researchers can trigger questions based on app usage or location: 'You just used Instagram for 30min. How do you feel?' 'You just left a coffee shop. How much did you pay?' This solution is offered by the German company Murmuras.

Outside of academic and commercial research, the use of experience sampling is rare. One consumer market example is Mood Patterns, a mood tracking app available for Android.

See also[edit]

References[edit]

  1. ^Bolger, N.; Laurenceau, J.P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York, N.Y.: Guilford Press.
  2. ^Csikszentmihalyi, M. (July 2014). Validity and Reliability of the Experience-Sampling Method. New York: Springer. p. 322. ISBN978-94-017-9087-1.
  3. ^van der Krieke; et al. (2015). 'HowNutsAreTheDutch (HoeGekIsNL): A crowdsourcing study of mental symptoms and strengths'(PDF). International Journal of Methods in Psychiatric Research. 25 (2): 123–144. doi:10.1002/mpr.1495. PMC6877205. PMID26395198.
  4. ^Larson, R.; Csikszentmihalyi, M. (1983). 'The experience sampling method'. New Directions for Methodology of Social and Behavioral Science. 15: 41–56.
  5. ^Hektner, J.M., Schmidt, J.A., Csikszentmihalyi, M. (Eds.). (2006). Experience Sampling Method: Measuring the Quality of Everyday Life. Sage Publications, Inc. ISBN978-1-4129-2557-0
  6. ^van der Krieke, L; Blaauw, FJ; Emerencia, AC; Schenk, HM; Slaets, JP; Bos, EH; de Jonge, P; Jeronimus, BF (2016). 'Temporal Dynamics of Health and Well-Being: A Crowdsourcing Approach to Momentary Assessments and Automated Generation of Personalized Feedback (2016)'. Psychosomatic Medicine. 79 (2): 213–223. doi:10.1097/PSY.0000000000000378. PMID27551988.
  7. ^Nielson, D. M.; Smith, T. A.; Sreekumar, V.; Dennis, S.; Sederberg, P. B. (2015). 'Human hippocampus represents space and time during retrieval of real-world memories'. Proceedings of the National Academy of Sciences. 112 (35): 11078–11083. Bibcode:2015PNAS.11211078N. doi:10.1073/pnas.1507104112. PMC4568259. PMID26283350.
  8. ^Mehl, Matthias J.; Tamlin, Conner L. (2013-10-01). Handbook of Research Methods for Studying Daily Life. ISBN9781462513055.
  9. ^Runyan, J. D.; Steenbergh, T. A.; Bainbridge, C.; Daugherty, D. A.; Oke, L.; Fry, B. N. (2013). 'A smartphone ecological momentary assessment/intervention 'app' for collecting real-time data and promoting self-awareness'. PLOS ONE. 8 (8): e71325. Bibcode:2013PLoSO..871325R. doi:10.1371/journal.pone.0071325. PMC3743745. PMID23977016.
  10. ^Wiebe, Douglas J.; Nance, Michael L.; Houseknecht, Eileen; Grady, Matthew F.; Otto, Nicole; Sandsmark, Danielle K.; Master, Christina L. (2016). 'Ecologic Momentary Assessment to Accomplish Real-Time Capture of Symptom Progression and the Physical and Cognitive Activities of Patients Daily Following Concussion'. JAMA Pediatrics. 170 (11): 1108–1110. doi:10.1001/jamapediatrics.2016.1979. PMID27617669.
  11. ^'PIEL Survey'.
  12. ^'PIEL Survey - Apps on Google Play'.
  13. ^https://pielsurvey.org/profile/survey-experience-sampling-method/
  14. ^Hofmann, W., & Patel, P. V. (2015). SurveySignal: A convenient solution for experience sampling research using participants’ own smartphones. Social Science Computer Review, 33, 235-253. http://journals.sagepub.com/doi/pdf/10.1177/0894439314525117
  15. ^'Archived copy'(PDF). Archived from the original(PDF) on 2016-07-05. Retrieved 2016-11-11.CS1 maint: archived copy as title (link)
  16. ^Conner, T. S. (2013, May). Experience sampling and ecological momentary assessment with mobile phones. Retrieved from http://www.otago.ac.nz/psychology/otago047475.pdf
  17. ^as available through, e.g., F-Droid catalogueArchived 2016-03-06 at the Wayback Machine
  18. ^'Expimetrics'.
  19. ^Blaauw; et al. (2016). 'Let's get Physiqual - an intuitive and generic method to combine sensor technology with ecological momentary assessments'. Journal of Biomedical Informatics. 63: 141–149. doi:10.1016/j.jbi.2016.08.001. PMID27498066.
  20. ^'Interactive Ambulatory Assessment - Project - movisens GmbH'.

Purdue Momentary Assessment Tool Download

Retrieved from 'https://en.wikipedia.org/w/index.php?title=Experience_sampling_method&oldid=993852449'

Purpose

The Purdue Pegboard aids in the selection and rehabilitation of employees for various types of manual labor by measuring 2 types of dexterity:

  1. Gross movements of the fingers, hands and arms.
  2. Fine fingertip dexterity necessary in assembly tasks.

Area of Assessment

Coordination
Dexterity

Cost

Not Free

Cost Description

$110-150

Purdue Momentary Assessment Tool Set

Diagnosis/Conditions

  • Parkinson's Disease + Neurologic Rehabilitation

Populations

  • The Purdue Pegboard Test is a rectangular board with 2 sets of 25 holes running vertically and 4 concave cups at the top. Small metal pegs are placed in the cup on the side being tested, with subjects asked to remove the pegs and place them vertically in the holes as rapidly as possible. The number of pegs placed in 30 seconds is scored.
  • The original application for the test was for testing the dexterity of industrial workers. It has since been used for testing of dexterity testing within various populations in the clinical setting, including children and adolescents.
  • The test takes about 30 seconds per activity for a total of 5-10 minutes including instruction. The test administrator compiles 5 separate scores from the complete test procedure, one for each battery:
    1) Right Hand (30 seconds)
    2) Left Hand (30 seconds)
    3) Both Hands (30 seconds)
    4) Right+Left+Both Hands (This is not an actual test, but a mathematical sum calculation)
    5) Assembly (60 seconds)
    (Lafayette Instrument Company User’s Manual, 2002)

Equipment Required

  • Purdue Pegboard Test
  • Instruction Manual
  • Test Board
  • Pins, Collars, Washers
  • Score Sheets
  • At least one testing table approximately 30 inches tall. The subject must be seated throughout the administration of the test.
  • Stopwatch

Required Training

Reading an Article/Manual

Age Ranges

Adolescent

13 - 17

years

Elderly Adult

65 +

years

Instrument Reviewers

References from the Parkinson’s disease population by Jeffrey Hoder, PT, DPT, NCS and the PD EDGE Task Force of the Neurology section of the APTA

Tool

ICF Domain

Body Function

Professional Association Recommendation

Recommendations for use of the instrument from the Neurology Section of the American Physical Therapy Association’s Multiple Sclerosis Taskforce (MSEDGE), Parkinson’s Taskforce (PD EDGE), Spinal Cord Injury Taskforce (PD EDGE), Stroke Taskforce (StrokEDGE), Traumatic Brain Injury Taskforce (TBI EDGE), and Vestibular Taskforce (Vestibular EDGE) are listed below. These recommendations were developed by a panel of research and clinical experts using a modified Delphi process.

For detailed information about how recommendations were made, please visit: http://www.neuropt.org/go/healthcare-professionals/neurology-section-outcome-measures-recommendations

Abbreviations:

HR

Highly Recommend

R

Recommend

LS / UR

Reasonable to use, but limited study in target group / Unable to Recommend

NR

Not Recommended

Recommendations Based on Parkinson Disease Hoehn and Yahr stage:

I

II

III

IV

V

PD EDGE

LS/UR

R

R

R

LS/UR

Recommendations for entry-level physical therapy education and use in research:

Students should learn to administer this tool? (Y/N)

Students should be exposed to tool? (Y/N)

Appropriate for use in intervention research studies? (Y/N)

Is additional research warranted for this tool (Y/N)

PD EDGE

No

No

Yes

Not reported

Considerations

Parkinson’s disease: Strong psychometrics. It is valid and reliable. It has been used in medication trials (Tan, 2003), post neurosurgery (Pal, 2000) and to measure dexterity during off times in PD (Brown, 1998). It was used to test dexterity during dual task performance (Proud, 2010). Correlated strongly to UPDRS total and motor (Proud, 2010). Dexterity decreases with increased severity of disease. Pegboard scores best correlated with bradykinesia and loss of dopamine per PET scan (Vingerhoets, 1997).

Do you see an error or have a suggestion for this instrument summary? Please e-mail us!

Normative Data

(Tiffin, 1948)

Normative data was established on factory workers who performed manual tasks for their occupation.

Bibliography

Brown, R. G. and Jahanshahi, M. (1998). 'An unusual enhancement of motor performance during bimanual movement in Parkinson's disease.' J Neurol Neurosurg Psychiatry 64(6): 813-816. Find it on PubMed

Pal, P. K., Samii, A., et al. (2000). 'Long term outcome of unilateral pallidotomy: follow up of 15 patients for 3 years.' J Neurol Neurosurg Psychiatry 69(3): 337-344. Find it on PubMed

Proud, E. L. and Morris, M. E. (2010). Bootable el capitan. 'Skilled hand dexterity in Parkinson's disease: effects of adding a concurrent task.' Arch Phys Med Rehabil 91(5): 794-799. Find it on PubMed

Tan, E. K., Ratnagopal, P., et al. (2003). 'Piribedil and bromocriptine in Parkinson's disease: a single-blind crossover study.' Acta Neurol Scand 107(3): 202-206. Find it on PubMed

Purdue Momentary Assessment Tool Set

Vingerhoets, F. J., Schulzer, M., et al. (1997). 'Which clinical sign of Parkinson's disease best reflects the nigrostriatal lesion?' Annals of neurology 41(1): 58-64.