JasonMorrison.net

Usability Testing

Usability tests can be seen to fall into two general categories, based on their aim: tests which aim to find usability problems with a specific site, and tests which aim to prove or disprove a hypothesis. This test would fall into the former category. A search of the literature will reveal that tests looking to uncover specific usability problems often use a very small number of participants, coming from Nielsen’s (2000) conclusion that five users is enough to find 85 percent of all usability problems. Nielsen derived this formula from earlier work (Nielsen and Landauer, 1993). Although there is much disagreement (Spool and Schroeder, 2001), this rule of thumb has the advantage of fitting the time and money budget of many projects.

Use of Eye-Tracking Data

In terms of raw data, eye tracking produces an embarrassment of riches. A text export of one test of Mealographer yielded roughly 25 megabytes of data. There are a number of different ways eye tracking data can be interpreted, and the measures can be grouped into measures of search and measures of processing or concentration (Goldberg and Kotval, 1999):

Measures of search:

• Scan path length and duration
• Convex hull area, for example the size of a circle enclosing the scan path
• Spatial density of the scan path.
• Transition matrix, or the number of movements between two areas of interest
• Number of saccades, or sizable eye movements between fixations
• Saccadic amplitude

Measures of processing:

• Number of Fixations
• Fixation duration
• Fixation/saccade ratio

In general, longer, less direct scan paths indicate poor representation (such as bad label text) and confusing layout, and a higher number of fixations and longer fixation duration may indicate that users are having a hard time extracting the information they need (Renshaw, Finlay, Tyfa, and Ward, 2004). Usability studies employing eye tracking data may employ measures that are context-independent such as fixations, fixation durations, total dwell times, and saccadic amplitudes as well as screen position-dependent measures such as dwell time within areas of interest (Goldberg, Stimson, Lewenstein, Scott, and Wichansky, 2002).

Because of the time frame of this investigation, the nature of the study tasks, and the researcher’s inexperience with eye tracking hardware and software, eye tracking data was compiled into “heat maps” based on the number and distribution of fixations. These heat maps are interpreted as a qualitative measure.

Methods

The goal of the study is to evaluate the usability of the web site and identify problem areas that might be improved.

Participants were solicited from a population of Kent State University graduate students in the School of Library Science (SLIS) and the Information Architecture Knowledge Management (IAKM) program via email. Five students volunteered, although one dropped out of the study at the last minute. Participants ranged in age from 25 to 35 with half female, half male. All had more than five years experience using computers and the web.

Participants were invited to the SLIS usability lab where they were informed of the procedure and asked for their consent. Once demographic data was collected, participants were calibrated on the eye tracking system and then asked to complete eight tasks using the web site. Participants were encouraged to “think out loud” while they attempted to complete each task, and their comments were recorded. Interaction with the web site was recorded by screen capture software and an unobtrusive eye-tracking device. Finally, participants were asked to fill out a short survey gaging their opinion of the site and it’s usability. Participants were free to stop at any time, and no incentive was offered for participation other than the opportunity to see the new lab and equipment.

Quantitative measures of usability include task completion, time required for task completion, and number of steps to completion. Participant comments and eye tracking data are used as qualitative measures.

Testing Difficulties

During the course of testing a number of problems arose with the eye tracking software, ClearView. At the conclusion of one of the four tests, the software locked up, making the computer unresponsive. Eventually it crashed, losing all test data for that participant. Consultation with a faculty familiar with the software confirmed that this is an unpredictable, but common bug in ClearView with no known workaround.

In addition, a deficiency was discovered in ClearView’s scanpath and hot spot visualization tools. Both overlay fixation data on top of screen shots of the web page the user was viewing. Unfortunately, the screen shots are not taken as the participant proceeds through each task. Instead the software remembers the URL of each page and then retrieves the screen shot at a later time. Many pages in Mealographer (and virtually any non-trivial web application) look different depending on whether or not a user is logged in, what the user has submitted in a form, and the presence or absence of cookies for session information. Therefore, many of the hot spot maps are laid over a version of the page dissimilar to what the participant had actually seen.

Findings

Task Completion Analysis

Overall, participants were able to complete the tasks 70% of the time. For the purpose of this evaluation, a task is considered complete only when the participant has found and used the expected feature for each task – often times, users were satisfied with their outcome, even though they had not used the most appropriate features. For example, two participants approached Task 1A by searching for a food they already knew was high in protein and low in fat, instead of finding and using the Healthy Food Search form. Table 1 displays the task list and completion rates. The tasks with the lowest completion rate were 1B, 6B, 7 and 8. It is interesting to note that the best-performing tasks include food search, meal entry, and user account functionality, which are the top items seen on the homepage when not logged it. This may be an indication of poor discoverability.

In general, the task completion rate indicates plenty of room for improvement.

Task	Completion Rate	1. A. Think of a food that you like. Use this website to find out how much protein is in that food.	4/4
1. B. Try to find a food that is high and protein but low in fat.	1/4
2. Please find your way back to the Mealographer homepage without using the browser’s Back button.	4/4
3. Use this website to figure out how much fat and fiber you had at lunch today.	3/4
4. Sign up for an account.	4/4
5. Now that you have an account, please enter in what you had for breakfast today. How many calories did you have at breakfast?	4/4
6. A. Did you have more Calories at breakfast or lunch today? Find a way to figure this out.	3/4
6. B. Let’s say that you wanted to see a summary of your nutrition each day this week. Try to find a report that shows a week’s worth of information and look up your calcium intake.	2/4
7. Let’s say you wanted to try to have more than 25 grams of fiber each day. What’s the best way to make sure that you are with this web site?	2/4
8. If you were going to use this web site often, what would you do to make entering meals easier? If you can think of something, go ahead and do it.	1/4
Total	70%

Table 1: Task Completion

	Completed Tasks	Incomplete Tasks	Number (recorded)	19	11
Average Time	1:56	2:37
Average Path Length	3.79	6.27

Table 2: Task Completion vs Performance Measures

 

Note that for the three participants with recorded times per task and path lengths, completed tasks took less time and involved a shorter path (see Table 2). This is logical, but with only three participants the correlation is not significant.

Individual completion rates, average times per task, and average path lengths are given in Table 3 along with user ratings of the site from the post-test questionnaire. It is interesting to note that user performance on task does not seem to correlate with user ratings (although with so few participants, correlations would not be statistically significant). The user with the highest completion rate gave the site the lowest ease of use score and lowest score overall, while the highest rating was given by a participant in the middle of the field for each performance measure.

Overall, the user ratings of the site were positive, but not outstanding. Although the ratings don’t correlate to the measures, many of the specific usability problems noted later in this report were noticed by the participants themselves as they used the site. Presumably changes to correct these problems would result in higher ratings.

Participant	Completion Rate	Avg Time per Task	Avg Path Length	Organization Rating	Ease of Use Rating	Design Rating
1	4/10	1:33	2.9	4	4	3
2	6/10	3:35	8.0	3	3	4
3	10/10	-		3	3	3
4	8/10	1:36	3.2	4	5	4
Total	70%	2:15	3.7	3.5	3.75	3.5

Table 3: Participant Performance and Site Ratings

 

Table 4 shows the task time and path length for each recorded participant broken down by task. Except for tasks 2 and 8, target path lengths for each task were set to the smallest path that would achieve the goal plus one, and target task times were set somewhat arbitrarily to 2 minutes. Task 2 was much simpler than the other tasks and task 8 was open-ended, allowing participants to enter favorites, usuals, or both. It is difficult to set a task time goal for an interactive web site without data from previous tests. The amount of time a user might want to spend on a particular activity before becoming frustrated or giving up may very based on a number of factors including user motivation, user enjoyment, and web site stickiness. The target of two minutes was chosen because it was thought to be fairly aggressive, meeting the goal of the project to make diet tracking quick and easy.

 

	Participant 1		Participant 2		Participant 4		Average		Target
Task	Time	Path	Time	Path	Time	Path	Time	Path	Time	Path
1. A.	1:10	3	1:55	4	1:33	4	1:33	3.67	2:00	3
1. B.	0:38	1	1:29	4	1:11	2	1:06	2.33	2:00	3
2.	0:08	2	0:17	2	0:17	2	0:14	2	0:30	2
3.	1:56	3	4:35	3	2:17	4	2:56	3.33	2:00	4
4.	1:18	4	1:19	4	1:56	5	1:31	4.33	2:00	4
5.	1:25	3	4:56	3	2:53	3	3:05	3	2:00	4
6. A.	1:51	3	5:26	14	1:52	4	3:03	7	2:00	3
6. B.	1:23	3	5:59	15	0:36	1	2:39	6.33	2:00	2
7.	1:16	4	3:29	8	1:30	3	2:05	5	2:00	5
8.	2:48	3	6:20	23	1:58	4	3:42	10	-	-
Total	13:53	29	35:45	80	16:03	32

Table 4: Performance Measures by Participant

 

Items in Table 4 in bold denote tasks in which a participant had not met the target, and those with gray backgrounds represent successfully completed tasks. Looking at average performance, only tasks 1B and 2 completely met all objectives. It would be reasonable to look for specific usability problems in the tasks for which each participant missed one or more target, and many are addressed in the Specific Usability Problems section below.

Hot Spot Analysis

In Illustrations 5, 6, and 7, hot spot maps of the Mealographer home page are shown for three participants (one, two and four, respectively). These images show the relative number of fixations by color, with green meaning at least one fixation and red meaning three or more, with fixations length set to a maximum of 100ms.

 

Illustration 5: Hot spot map for participant 1

Illustration 6: Hot spot map for participant 2

Illustration 7: Hot spot map for participant 4

These hot spot maps illustrate some of the observations that can be made with eye tracking that might be missed by other usability testing methods. For example, participant one did not read the light blue help boxes at the bottom of the page whereas two and four did. The participants fixated on many of the red “* required” labels, but only briefly - this may mean that they served their purpose in alerting users without causing confusion or additional concentration. Note the large red areas under the drop-down boxes for “Month” and “Question.” Users had to fixate more on these controls than on many of the text fields.

Illustration 8: Scanpath for participant searching for “coffee”

Illustration 9: Scanpath for expert searching for “tea”

Illustration 10: Scanpath for participant searching for “coffee”

Scanpath Analysis

The scanpaths in Ilustrations 8, 9 and 10 show the result of users entering in a search term on the meal entry form and searching for the proper item to add to their meal. The two on the left represent searches for “coffee” by two participants, novice users of the site. The one on the right is a search for “tea” by the researcher, and can be considered an example of expert use. Note that the paths of both novice users are both much longer than the expert, and that the convex hull area would be larger as well, despite the fact that the desired item is located in a similar position on all three. The novices need to gaze at each item on the list in order, and require more gazes on likely items in order to make a decision. In all three cases, the long, spread out gaze paths indicate that desired items are not near the top of the results, and are not readily apparent. Improvements should be made to the search engine and the results display, perhaps highlighting search terms within each line.

Specific Usability Problems

A list of specific usability problems has been compiled from the participant performance results in Table 4, observations made during the test by the researcher, and participant comments. Problems are organized by task.

Task 1A – Food search

Search results do not always match user expectations. Multiple participants expresses confusion or had to refine their search to get to the food they were looking for. As the tagging system grows, it will help to address this issue.

Search results are hard to scan. Participants seemed lost in all the text in the results. This can be addressed by highlighting the search terms within the results and visually dividing items from each other.

The ordering of nutrients on food pages might not be intuitive. The food pages were designed to match the ordering used on product packages, but some users needed extra time to find what they were looking for. One participant said she expected them to be in alphabetical order.

Task 1B – Healthy food search

Users did not notice the healthy food search function at first. Three users tried using the food search to search for a food they already knew was high in protein and low in fat, at least at first, and another was satisfied with the food he had just searched for by name. The healthy food search could be given more prominent placement, perhaps above the scroll on the search results page.

Task 3 – Meal entry using the quick form on the homepage

One user did not find the meal form on the homepage. Participant 1 used the food search instead, explaining that she had only had one thing to eat. Since the other found the form easily, action might not be needed to correct this.

Results for each food entered in the form do not match user expectations. One participant, for example, entered “rice” but did not find plain rice in the dropdown on the next page. This is another search engine difficulty.

Users could have refined their terms, but did not see a way to do so. In fact they would have had to go back to the homepage and submit the new terms. An entry box could be added to the results page to allow users to refine their terms.

Some users left some fields blank, and did not get useful results. One user left all the “Units” fields blank and the results showed a meal with no nutrition. It took some time for that user to figure out the cause of the problem. Validation hints could be added to the form, and units could default to one instead of blank or zero.

Task 4 – Account creation

Meals entered by visitors could be lost if they do not go directly to create a new account. One user missed the link to create an account from the meal page and went to the homepage first. This is a perfectly valid action. The ID of the temporary meal record in the database should be associated with the user session so visitors do not have to re-enter meals later.

Task 5 – Meal entry

It is not immediately clear how to use the meal entry form. Three users tried to click on the notepad at first, expecting it to work similarly to the quick meal form. Although both did figure out to use the search form to find and then add items, this could be improved. Currently when the form first loads, the search results frame is blank. An arrow graphic and some explanatory text might make use of this form more clear. It might also make sense to have only one meal entry form, mixing the strengths of each.

Users did not always try to modify search terms to improve results. At least one user suggested good modifiers when thinking out loud, but did not try any of them. There is text in the results that suggest trying again, but it could be made more clear.

Food search results were difficult to scan. Participants seemed to take a long time finding the item they wanted on the list (see Illustrations 8, 9, and 10 for scanpath diagrams). One participant suggested adding bullet points to better mark items in the list. Search terms could also be highlighted in the results.

Users are not sure how to enter complex or compound food items. One user tried “salad” and “caesar salad” but compound items with many variable ingredients like salads and sandwiches are left out of the database. Some instructions could be added. This might also be solved by the addition of a food or recipe entry system.

Some users left some fields blank, and did not get useful results. Like the quick meal form, validation hints could be added to the form.

Task 6A – Use of daily report

Some users didn’t think to look for a report system. Three users used pen and paper to compare meal totals instead, although two did later find the daily reports. The existence and functionality of the reports could perhaps be made more clear, and additional links might make the reports more discoverable.

Reports (and many other sections of the site) are not identifiable from browser history.

Participant 2 had a particularly difficult time finding the reports using the browser’s history function. Page titles were very similar, and he tried nine different links before trying another tactic. This is important because it means many pages would not make good bookmarks either. Better, more succinct page titles need to be used on interactive pages.

Reports cannot be accessed when not logged in. In the course of using the browser history, participant 2 accidentally logged himself out without noticing. This made it almost impossible to find the reports, and when he did find the reports they did not have any data. Care was taken to ensure users could not alter other users data, or make entries without logging in, but dynamic pages such as the reports should clearly remind users to log back in rather than displaying zeros. Once that is done links to the reports could be added to the tour page.

Task 6B – Use of weekly report

Users were not immediately aware of the weekly report. The control to switch to the weekly report is located below the scroll on the report page. It could be moved up or a simpler control could be added to the top of the page.

Task 7 – Goal creation

Users did not think to look for goal-setting functionality. Two users reported being happy just using the reports to track progress, though one later did find the goal functionality. Another user overlooked the link to create a goal at least once before finding it. Goals should be better integrated into the report pages.

The list of goals was confusing when no goals were listed. A new user looking at the list before entering sees the column headings but no indication that this is list with zero items. The list should not appear when no goals exist.

Some terms are unfamiliar to users. One participant did not know what “DRI” and “Daily Value” meant when entering a goal. Tooltips or links to a FAQ could be used to help inform users.

Task 8 – Use of usuals and/or favorites

The list of favorites was confusing when no favorites were listed. This is also true for usuals. The list should not appear when no favorites exist.

Favorites and usuals were not discovered by most participants. Three participants either did not think to look for such options or did notice them while using the site. Favorites and usuals need to be better integrated into the rest of the site. It might be helpful to indicate existence of favorites on meal page, even if user has no favorites. Also, an “add this food to my favorites” link could be added to food pages.

 

 

 

References Cited

 

Gibson, J. J. (1979). The Ecological Approach to Visual Perception. Hillsdale, NJ:Lawrence Erlbaum Associates.

 

Goldberg, J.H., Kotval, X.P., (1999). Computer interface evaluation using eye movements: methods and constructs. International Journal Of Industrial Ergonomics 24, 631–645.

 

Goldberg, J.H., Stimson, M.J., Lewenstein, M., Scott, N., Wichansky, A.M. (2002). Eye Tracking in Web Search Tasks: Design Implications. Proceedings of the 2002 symposium on Eye tracking research & applications. New York: ACM Press. pp. 51-58.

 

Golder, Scott A., Huberman, Bernardo A. (2006). Usage patterns of collaborative tagging systems.

Journal of Information Science, Vol: 32, Issue: 2, April 2006, pp. 198-208

 

Lindgaard, Gitte, Fernandes, Gary, Dudek, Cathy, and Browñ, J. (2006).

Attention web designers: You have 50 milliseconds to make a good first impression. Behaviour and Information Technology, Volume 25, Number 2, Number 2/March-April 2006, pp. 115-126(12).

 

Nielsen, Jakob. (2000). Why You Only Need to Test With 5 Users. Alertbox March 19, 2000. Retrieved May 15, 2006, from http://www.useit.com/alertbox/20000319.html

 

Nielsen, Jakob and Landauer, Thomas K. (1993). A mathematical model of the finding of usability problems. Proceedings of the SIGCHI conference on Human factors in computing systems. April 24-29, 1993, Amsterdam, The Netherlands. pp. 206-213.

 

Norman, Don. (2004). Affordances and Design. Don Norman’s jnd.org. Retrieved May 15, 2006, from http://www.jnd.org/dn.mss/affordances_and.html

 

Renshaw, J.A., Finlay, J.E., Tyfa, D., Ward, R.D. (2004). Understanding visual influence in graph design through temporal and spatial eye movement characteristics. Interacting with Computers. Vol: 16, Issue: 3, June, 2004. pp. 557-578.

 

Spool, J. and Schroeder, W. (2001). Testing Websites : Five Users is Nowhere Near Enough. CHI ‘01 extended abstracts on Human factors in computing systems. New York: ACM Press. pp. 285-286.

 

Stafford, Tom and Webb, Matt. (2005). Mind Hacks: Tips and Tools for Using Your Brain. Sebastopol, CA:O’Reilly Media, Inc.

 

 

 

Sphere: Related Content

Abstract

Diet can have a great effect on health, but few people keep track of what they eat each day, let alone how much fat, protein, Calcium, or other nutrients. Although most food items have nutrition information printed on the packaging, few people can tell you whether or not the 10 grams of fat in their candy bar is acceptable, or whether it has put them over the edge.

In this project the author assumes that a big part of the reason people do not keep track of their diet is that there is no easy way to do so. The final product of this project is Mealographer, a simple interface that allows users to enter in the foods and meals they eat each day, set simple nutrition goals, and see reports of their progress. Mealographer was created by implementing a large number of improvements to the product of a previous investigation, WhatYouEat. A usability test was conducted to evaluate Mealographer and find specific usability problems.

Previous Work – The WhatYouEat Project

Mealographer inherits much of its functionality from a previous project, titled WhatYouEat, part of an individual investigation from fall, 2005. The original project had two goals: to create an application that allows users to track their dietary intake, and to make the application as easy to use as possible.

WhatYouEat allowed users to record their meals, set simple goals for different nutrients, and

track their diet through simple reports. Supporting functionality included a simple user sign up and login system, and systems allowing users to indicate favorite foods and “usuals” – foods eaten on a regular basis.

WhatYouEat was demonstrated informally to several groups and an informal usability test was run with four participants. Although this style of evaluation was not rigorous, users were asked to use the site and comment on any confusion or difficulties. Many users also commented on design and additional functionality. Usability issues included difficulty in:

Targeting

• Even with a large screen size and large font, it was hard for one subject to click on fields before entering text.
• Field labels were used to enlarge the clickable area. It may be possible to have the cursor will default to the first field.

Layout

• Two users were a little confused about the two-column layout of input forms.
• A thin line was added to help make the grid more clear.

Forms

• Three users forgot to set the meal date at least once. The submit button was easy to miss. One user hit enter to submit the search form and didn’t expect the entire meal to be submitted. There were problems using the back button.
• The submit button was made more visible
• The forms were be broken up so that the submit button for a particular field only submits that field.
• Required fields could be made more clear with a symbol and some JavaScript.

Labeling

• Some labels were unclear or hard to read. In particular, dates presented in yyyy-mm-dd format and names of nutrients.
• The labels should be changed to reflect user expectations.

Measurements

• Many users had a hard time determining how much they had eaten, or understanding how much food each measurement amount actually represented. Few of them knew what an ounce or gram of a given food looked like, or how much of non-fluid items made up a cup.
• Some graphic representation of food amounts should be available in the system, as well as a conversion application. See Future Plans for more information on the approach to this problem.

Missing items

• Users more than once looked for food items that did not appear to be in the database at all. This included brand-name items or items from specific restaurants. This is a limitation for the USDA database.
• There is no simple or quick solution to this problem. See Future Plans for more information on the approach to this problem.

Mealographer Features and Functionality

Mealogapher includes a number of improvements to the existing WhatYouEat functionality as well as some additional features. Major new and improved features include:

Information Design

WhatYouEat had simple, somewhat consistent design that did not convey much information about the content of each page. Mealographer was designed to

Action blocks – on each page where a series of actions are appropriate next steps, those actions are placed in a lighter-colored box with a descriptive title.

Simple icons – small icons are used throughout the site to quickly convey small bits of information. For example, purple arrows replace list bullets on action items in blocks. The Calendar employs icons to indicate whether or not a goal has been met.

Nutrition information – The nutrition information for foods and meal totals has been designed to match the familiar nutrition information boxes on foods.

Link highlighting – Many links, including the navigation bar and action items, have been given a highlight when the user’s mouse hovers over.

Language

Care was taken to make the verbiage used on the site straightforward, concise, consistent, and in line with the site’s brand. For example, when users are presented with options they are asked, “would you like to… Add another item to your usuals.” Users are addressed directly by the site, and not given static items like “Add usuals,” or “My usuals.”

Home Page

The homepage has been redesigned to be a central gateway, allowing easy access to site features, as well as an introduction to the site. The homepage has two different configurations: a “general public” version for site visitors and new users that have not yet logged in, and a “personal” version for logged-in users.

Public Homepage

The general public homepage was created with two goals in mind: first, to introduce new users to the site; and second, to intrigue and draw them in by giving them features to try out. The first goal makes sense since new users must know what the site does if they are to ever use it. The second goal was inspired by the fact that when presented with a choice, people generally chose to stay with the status quo (Stafford & Webb, 2005, p. 246). The status quo in effect when a user reaches a new, possibly complex web application for the first time is simple—do nothing. Mealographer presents an additional challenge, because most people do not actively track their diets. Using a diet-tracking site will be a big divergence from the status quo for many new users.

Illustration 1: Public home page

The public homepage is intended to break visitors out of the status quo by giving them a simple, quick way to try the site out. The interface must be immediately apparent because users can make decisions about web designs in as little as 50 ms (Lindgaard, Fernandes, Dudek, and Brown, 2006). The way to make it apparent that a user can interact with a site is by presenting clear affordances, or visual impressions that imply likely use (Gibson, 1979, p.127). Cultural, learned conventions can be used to help users perceive affordances on a computer screen (Norman, 2004).

Visitors are presented with two elements to try. The first is the “What did you eat today?” quick meal entry form, which allows users to begin using the site’s functionality with as little investment of time and effort as possible. The second is a “Search for a food” form that acts as a simple search engine. In order to take advantage of cultural conventions, a multi-line text area was chosen to allow visitors to type in items they’ve eaten, and a single-line text input was used for the food search.

Personal Homepage

The personal homepage both acts as a central location for registered users to find and discover functionality and a top-level view of the actions they are most likely to want to do at the current time. Entering meals and watching diet changes are both activities with strong temporal dimensions. Accordingly, a daily “to do” calendar was chosen as the metaphor for the list of meals entered and yet to be entered, and a monthly calendar is used to display a high level view of progress. Both employ simple visual cues to give users an immediate impression. Completed activities are marked off the to do list with check marks, and smiling or frowning faces are displayed on the days of the monthly calendar to show if a goal has been met for each day.

Illustration 2: Personal home page

The rest of the items on the personal homepage are organized into the action blocks “Connect to People,” “More Options,” “Goals and Reports,” and “Foods and Facts.”

Food Pages

Nutrition information about the food items in the database was used to generate a detailed page for each food. The pages include the nutrition information box, a food search form, and links to other similar foods in the database. They serve three purposes: first, users can search for and find information about a specific food. Second, the pages can be linked to from multiple locations in the site, wherever users might see a food name and want additional information. Third, the pages would be available to outside search engines, and might therefore act as landing pages for visitors and new users.

Illustration 3: Example food page

Although the information is from the database, these pages are not dynamic. Instead, static HTML pages are generated from the database and then saved and uploaded to the web host. Nutrition information is unlikely to change, so this technique saves web server processing time and database access.

Food Search and Folksonomies

One of the most apparent usability problems of WhatYouEat was the food search. Food search is a standalone function and is an inherent part of meal entry, so it is critical that it be improved. Problems stem from three main areas:

Missing items – although the USDA database contains thousands of foods, it is missing many items users are looking for. Many brand-name items are not in the list, as well as ethnic foods and composite foods (there is no general entry for “sandwich” or “salad”). Possible fixes to this problem include searching for and entering new foods, allowing users to enter new foods, and allowing users to build recipes from multiple foods. None of these options were implemented in this time frame.

Naming and Labeling – the USDA database uses very precise names for many foods. These names are very useful for professionals, but do not match language used by typical users. For example, a user might search for “Coke,” when the database has “Carbonated beverage, cola, contains caffeine.” The solution to this problem implemented in Mealographer is a behind-the-scenes tagging system to build up a folksonomy for foods. Tagging systems can allow for both majority consensus on labels or search terms while maintaining minority opinions as well, and allow sites to harness information entered by users for personal use for wider benefit (Golder and Huberman 2006).

The tagging system implemented in Mealographer does not directly ask users for keywords for each food—that would be an extra step, and it is hard to see the immediate benefit a user would receive for their tagging work. Instead, as users perform searches, the system silently remembers the keywords used and the food ultimately chosen and builds tags automatically. A robust tag set requires many users, so it may be difficult to judge the value of the tagging system at this time.

Technical Limitations – by default, the search engine included in MySQL uses a fairly simple search engine which does not meet high expectations users have gained from using advanced engines like Google. In addition, the MySQL full text search ignores all works three characters or less. This can be useful in screening out words like “and” or “for,” but is less helpful when a user searches for “Big Mac.” As the folksonomy grows and tags are added, these limitations might become less important.

Healthy Food Search

In addition to searching for foods by keyword, an interface was created to allow users to search for foods by nutrient content. For example, a user can search for foods that are high in protein, or low in fat and high in fiber. Users may also filter by food group.

New User Invitations

A simple interfaces has been added to allow current users to send invitations to others via email.

Navigation

One finding from the presentations of WhatYouEat was the strong expectation among users to have a navigation bar near the top or left side of the screen. A navigation bar has been added with links to “Home,” “Foods,” “Meals,” “Favorites,” and “Reports.” Because the latter three items only work for registered users, those items are removed from the navigation bar for visitors.

Navigation is also facilitated by the user of appropriate action item links in an action box on most pages. For example, after a user enters a meal, they are given four options: “Make changes to this meal,” “Enter in a new meal,” “See your daily report,” and “Return home.” Users can use these to move from activity to activity in a logical progression.

Help and Validation

Originally Mealographer was going to include a help system such as a FAQ page. In order to more directly user test the interface, the help system was left out. A few new features do provide some user support, however:

Form hints and validation – On several forms, such as the new user form, required items are marked with “* required.” As the user completes each form field, the text turns from red to green.

Bug reports – Users are able to report bugs using the “Report a Problem” link in the footer of each page.

Tour – a page was added explaining the major functions of the site. The tour is shown as an action link after a new user signs up and is available on the footer.

Reports

WhatYouEat had a simple reporting system that allowed users to see bar charts for a particular nutrient for a particular time period. A user could see their total Calcium intake for each meal on a particular day, each day of a week, or each day of a month. A number of improvements have been made to the report system for Mealographer. Users are given a better system to see if they have met their goals, with bars falling short colored red and those meeting the goal in green. The monthly report has the calendar from the personal home page integrated into it. The reports now support a complete drill-down capability, with users able to go from month, to day, to nutrition details about a single meal.

Illustration 4: Nutrition information report for a meal

The nutrition information report for meals is a new feature, reached via the other reports and displayed after a meal is entered. It includes a nutrition information box for the meal, a list of foods and amounts eaten, and a meal size graphic relating the size of the meal to an equivalent number of apples. The meal size graphic can help users judge their portion sizes.

Charts and Statistics

One benefit of building Mealographer as a web application is that the data for all users is available in the same database. This database allows the generation of interesting charts and statistics. For example, queries could be written find the most popular food in a particular state or city, the average calories consumed each day by users, or the weight of all meals entered all together.

Because of the limited number of users, only one such feature was implemented at this time – the popular foods list. The homepage includes this list, ranked by the number of times the food was used in a meal.

Social Features

Eating is a social activity, and dietary changes are often prompted and monitored by medical professionals. Two features were partially implemented (and are currently disabled) in Mealographer to address these facts. Users would be able to form social groups by inviting each other into their “circle,” and another feature would allow users to make their data available to their doctor, dietitian, or trainer.

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