Category Archives: HFES Annual Meeting

HFES Conference Part 6: Health & Human Factors

The medical domain is an area where human factors research is very active. Here are some highlights from the conference.

Health Records

The following two presentations/proceedings papers examined Personal Health Records (user-maintained medical records):

Improving the user interface and adoption of online personal health records. (2009).  Peters, K. A., Green, T. F., & Shumacher, R. M.

This paper was a usability evaluation of some of the most popular PHR web apps on the web (Google Health, Microsoft Health Vault).  Thirty participants completed a variety of tasks using both systems while the number and types of errors were examined.  In addition, qualitative analysis examined adoption factors (perceived usability, utility, etc).  We’ve blogged about this study (which is also available as a white paper from UserCentric).

Examining non-critical health information seeking:  A needs analysis for personal health records. (2009).  Price, M. M., Breedlove, J., Pak, R., Muller, H., & Stronge, A.

This was in-progress work that my student and I conducted.  We were interested in the generic adoption of health IT by older consumers.  While PHR development has proceeded full-steam, there has not yet been research examining the utility of these systems for older adults–those who may benefit the most from accurate, user-maintained, always-available health information.  The study was a diary study of the kinds of health-related questions that older adults (those over age 60) have in the course of 2 weeks.  Other dimensions were number of health questions, severity, strategies used to answer the questions, and attitudes toward technology assistance.

Some papers examined health records from the perspective of the health care provider (Electronic Health Records; EHRs):

Healthcare workers’ perceptions of information in the electronic health record. (2009).  Russ, A.L., Saleem, J. J., Justice, C. F., Hagg, H., Woodbridge, P. A., Doebbeling, B. N.

The researchers were interested in understanding why EHR adoption by health care workers was not as swift as one would assume.  They conducted 14 interviews of health care workers to better understand how EHRs could be designed to improve health care work flow.  Seventeen major aspects of EHRs that prevented adoption were identified but only 5 were discussed in detail:  Customizeability (EHRs were difficult to customize or not customizeable), Prioritized (EHRs did not present ‘priority’ information; users often used external programs to add this information), Trendable (long term trends were not easily visualizeable), Locatable (classic information overload; there is just too much information and not an easy way to navigate it), and Accessible (technical issues such as login problems, network issues).  The paper outlines specific examines of “work-arounds” used by workers to overcome some of these issues.

Technology & Health

On to the more generic topic of health care IT, there was

Documentation in a medical setting:  effects of technology on perceived quality of care. (2009).  DeBlasio, J., & Walker, B. N.

imagesThis paper found that perceptions of the technology used by the health care provider significantly influenced the patient’s perceived quality of care. In their study, they had undergraduates view video clips of a doctor taking notes from a patient. The researchers manipulated the method of note-taking (mental notes, paper, PDA, desktop PC, and wearable PC) and angle of view (head-on and offset 90 degrees). The lowest quality of care condition was desktop PC. The highest QoC conditions were wearable PC or mental notes)–presumably reflecting the negative perceptions of “obvious” technology.


The nurse’s role in health care was highlighted in many papers.  Two noteworthy papers (because they both deal with prospective memory) were:

Distributed prospective memory:  an approach to understanding how nurses remember tasks. (2009).  Grundeiger, T., Sanderson, P. M., MacDougal, H. G., & Venkatesh, B.

Prospective memory in the nursing environment:  effects of type of prospective task and prospective load. (2009).  Fink, N., Pak, R., & Battisto, D.

HFES Conference Part 5: Automation & Trust & Google Maps

During the conference I had a very personal experience with the effects of automation reliability on trust and subsequent behaviors.  First, a bit of background.  There is a large body of research examining how humans interact with automated systems (Global positioning systems, for example).  Human-automation interaction is quite complex; being affected by many factors.

Julian Sanchez (of MITRE) presented a poster at the conference summarizing the literature; presenting how the many variables of human-automation interaction relate to each other (figure 1).  One factor being extensively investigated is the issue of how much the user/operator (you & me) trusts the automation.

Figure 1. Conceptual Model of Human-Automation Interaction

I have used Google Maps on my phone extensively; and in the many cities I’ve used it, it has been a reliable tool for directions.  Since the phone includes a GPS chip, it can track my movements as I walk showing me my distance to my destination.  However, it failed miserably in San Antonio…twice.  First, I tried to find a restaurant near the River Walk and following the directions led me to go almost in the complete opposite direction.  I was so confident in Google we spent 20 minutes walking around until we asked a local policeman for directions. One reason I was confident was that as we walked toward our destination, the phone confirmed that our position was nearing the GPS destination.


The second failure was when we tried to find the Cowboy bar.  Automation researchers would say that I was complacent–I over-relied on the automation which indicted that my trust was not calibrated correctly.  My high level of trust came from thinking, “San Antonio is a big city, it must be fully and accurately mapped…” as well as past successful navigation attempts. This is one consequence of ultra-high reliability systems: the effect they have on users expectations and trust. Ever since my return, I’ve needed to use Google Maps (on the web or phone) and I have found myself very uncertain of the stated locations and directions offered by Google Maps.  I confirm Gmaps using the competing service (Bing Maps).

Sanchez, J.  (2009).  Conceptual model of human-automation interaction.  Proceedings of the Human Factors and Ergonomics Society 53rd Annual Meeting.

HFES Conference in San Antonio, Part 4 – Hearing and Understanding

The paper described in this post was part of the Aging Technical Group sessions at HFES.

Hearing Levels Affect Higher-Order Cognitive Performance – Carryl L. Baldwin, George Mason University

46324162_87eda38e57 / CC BY-NC-ND 2.0

Perhaps I was excited by this talk because I could see how the information could be used in the book Rich and I are working on. This presentation was a fascinating exploration of the types of trouble adults over sixty-five might have with auditory interfaces. These problems are not necessarily related to the function of the ear: in general, older adults may have more poor hearing, but it is due to environmental exposure rather than aging of the ear. Many older adults show no detectable hearing loss, yet still have trouble with auditory interfaces, as found by Carryl’s experiment.

An important contribution of this paper was the connection found between decline in a sensory ability (hearing) and decline in cognitive ability, even on tests that had no auditory elements. Carryl addressed this years ago in her article Designing in-vehicle technologies for older drivers: application of sensory-cognitive interaction theory. Essentially, when most of us study cognitive aging, we either omit or control for sensory ability. For example, in my work, all older adults must have corrected vision of 20/40 or better and if there is any auditory component, must meet hearing level requirements.  Sensory ability may well predict their task performance, but I do not study it.

Carryl pointed out in her talk that even older adults with no measurable hearing loss showed worse working memory capacity as stimuli got harder to hear. This was true for younger adults as well, but the older listeners were harmed differentially worse as the stimuli dB levels decreased.  It isn’t hard to see why telephone menus and other auditory interfaces can be so frustrating: what requires more working memory than a softly spoken voice menu with 9 options? Eek.

Take home messages:

“The observation that scores on an assessment of working memory capacity decreased in young listeners indicates that hearing level, irrespective of age, can impact performance on aurally presented working memory tests.” (p.124)

“…functional hearing level may play a substantial role in the performance of older adults.  Subclinical hearing loss may result in the need to expend greater effort to process test stimuli – thus compromising performance in higher order stages.” (p.124)

Primary Sources

Baldwin, C. (2009). Hearing Levels Affect Higher-Order Cognitive Performance.
Baldwin, C. (2002). Designing in-vehicle technologies for older drivers: application of sensory-cognitive interaction theory. Theoretical Issues in Ergonomic Science, 3, 307-329.

HFES Conference in San Antonio, Part 3 – Health/Internet…and ROBOTS!

One of my major interests at the moment is in the use of technological tools (primarily the Web) in the management of health.  So it was with great pleasure that there was so much research on this topic (I will mention more in future posts).

The first was presented in the Aging session (where Anne was program chair).  Jessie Chin and her co-authors were interested in a cognitive dilemma faced by older adults.  With increasing age, fluid cognitive abilities (those used in rapidly changing situations like working memory) decline with age.  These abilities seem particularly crucial when using the web as well as other tasks.  However, it is known that older adults often compensate for fluid ability declines by capitalizing on pre-existing knowledge (so called crystallized knowledge) which increases with age. (For an adequate and publicly available elaboration of the fluid/crystallized distinction, see the Wikipedia entry).

09AMlogoThe researchers examined the relationships between fluid and crystallized intelligence on illness knowledge in older adults (hypertension knowledge).  Consistent with their hypothesis, they critically found that illness knowledge (a form of crystallized intelligence gained through time) was a significant predictor of illness knowledge and that this knowledge may moderate the reduced fluid abilities.

Continue reading HFES Conference in San Antonio, Part 3 – Health/Internet…and ROBOTS!

HFES Conference in San Antonio, Part 2 – Eliciting Knowledge Structures

I‘d like to highlight some of the talks I enjoyed last week and point our readers to their research.

First up, we have:

The Influence of Rating Method on Knowledge Structures.
Chad C. Tossell, Rice U.; Brent A. Smith, U.S. Air Force Academy; Roger W. Schvaneveldt, Arizona State U., Polytechnic

This talk was a great introduction to understanding how we organize information in the brain as we change from novices to experts in a task. This isn’t something I’ve done in my work, so I learned a great deal in the 15 minute presentation.

In a nutshell, one can use a technique called Pathfinder to see how people link concepts associated with a job or task. These links can be analyzed to see how people change the organization of the knowledge as they become experts. For example, a novice would have a very different understanding of how the parts of an engine depend on one another, or at best only show topical links between parts of the system. An expert mechanic would organize the parts of the engine differently. Usually the knowledge structure of one expert is very similar to that of other experts (while novices vary greatly.)

Unfortunately, using the Pathfinder technique is time consuming and arduous. The current talk focused on simplifying the technique while preserving the integrity of the findings.

Continue reading HFES Conference in San Antonio, Part 2 – Eliciting Knowledge Structures

HFES Conference in San Antonio, Part 1

Anne and I just got back from the Human Factors and Ergonomics Society conference held in San Antonio.  We plan on posting some snippets of posters/talks that we found interesting in an upcoming post.  But in the mean time, here is a panorama of the view from our hotel.


Being in San Antonio, TX, we also visited a Cowboy bar complete with a mechanical bull.  Being human factors geeks, we had to take a look at the surprisingly simple controls used to create such complex movements.  And no, we did not analyze the controls or interview the operator 😉


Live… from New York, it’s HFES!

Richard and I are currently attending the Annual Meeting of the Human Factors and Ergonomics Society. I thought I’d report on some of the interesting work we saw this week.

First, a shameless plug for research conducted at my own university. David Sharek and Mike Wogalter presented data on how clueless and careless the “wired” generation can be when it comes to computer security. Briefly, undergraduates treated real and fake “security” announcements on PC’s similarly: by clicking “ok” to whatever it asked them. My mother has personal, recent experience that this is a GREAT way to get spyware and viruses on your computer. You might think that 20 year-olds would not be so easily fooled… but then David and Mike wouldn’t have their study buzzed on: Slashdot, ScienceDaily, Reddit, and the BBC.

Second, we have a new “technical group” called Augmented Cognition. Talks in this session included two talks on using physiological markers to predict display needs (an area long pursued without as much progress as one might hope). Check out “Using physiological measures to discriminate signal detection outcome during imagery analysis” and “Biomarkers for effects of fatigue and stress on performance: EEG, P300, and heart-rate variability.”

There is much more, too much to mention individually, but I’d like to invite the readers to comment on their personal favorites from the week.

  • Berka, C., Johnson, R., Whitmoyer, N., Behneman, A., & Popovic, D. (2008). Biomarkers for effects of fatigue and stress on performance: EEG, P300, and heart-rate variability.Human Factors and Ergonomics Society, Santa Monica, CA, 192-196.
  • Hale, K. S., Fuchs, S., & Axelsson, P. (2008). Using physiological measures to discriminate signal detection outcome during imagery analysis. Human Factors and Ergonomics Society, Santa Monica, CA, 182-186.
  • Sharek, D., Swofford, C. & Wogalter, M. (2008). Failure to recognize fake internet popup warning messages. Human Factors and Ergonomics Society, Santa Monica, CA, 557-560.