The authors want to thank Anouk Geelen and Arvind Datadien for their key role in the development of Traqq. Furthermore, the authors would like to thank Romy Willemsen for her assistance in the data collection and the data analysis in the usability study. Finally, the authors want to thank the experts and participants for sharing their experiences and opinions throughout the process. The development was executed by Wageningen University and Research and partly funded by the Ministry of Agriculture, Nature and Food Quality and industry, in the context of TKI Agri&#38;Food PPS - project Smart Food Intake (AF16096).

The authors have nothing to disclose.

This paper presents the iterative developmental process of the smartphone-based dietary assessment app Traqq. Balancing the required level of accuracy and user-friendliness posed the following main challenges in the development of the app related to decisions on 1) data entry (i.e., selecting the most accurate method for food identification and portion size quantification), 2) food composition data (i.e., selecting an accurate database and creating a full-fledged food list), 3) customization options (i.e., flexibility in food list, portion size quantification, and recipes), and 4) validation (i.e., against traditional methods and/or independent measures)3,50. During the literature review, five validated and fully automated, smartphone-based, dietary assessment tools developed for research were identified3, namely My Meal Mate4, Electronic Dietary Intake Assessment (eDIA)7, Easy Diet Diary8, Electronic Carnet Alimentaire (e-CA)5, and Eat and Track (EaT)6.
Owing to the level of automatization of these five dietary assessment apps as well as this app, researcher burden and costs substantially decreases while data completeness increases compared to traditional dietary assessment methods. Additionally, this app, in turn, differs from the five existing dietary assessment tools in terms of flexibility. Specifically, whereas existing apps are all based on the food record method, this app can be used as a food record as well as a recall. Moreover, whereas the design of these apps is fixed, Traqq has the major advantage that it can be modified to fit different research purposes (e.g., dietary assessment method, food list, sampling schemes, additional questions)3,50. Conversely, other existing dietary assessment apps contain valuable features, which are not implemented in the app (yet). To illustrate this point, some apps allow the user to take photographs of their food for food recognition and portion size estimation such as the semi-automated, technology-assisted dietary assessment (TADA) system51,52.
Participants in the usability study also indicated that the use of photographs could be a valuable addition to aid portion size estimation. However, there were still too many challenges to be addressed to implement such a feature at this stage, e.g., specifying and guiding with respect to the photographic angle (i.e., to assess depth), the need for a reference maker (i.e., to correct for sizes and colors), the essential before and after photo (i.e., to assess consumed amounts), and on how to process recipe dishes. Due to these technical challenges, the existing image-based dietary assessment apps are still semi-automated, which means manual image review must be done by the user, the researcher, or both51,52. Technological advances, such as crowdsourcing and machine learning, have the potential to improve the use of food images for dietary assessment53,54. In the future, these options will be explored to further improve the app. The developmental process of the app was characterized by various critical steps. First, a formative research step was completed wherein the scientific concepts underpinning the rationale for app creation facilitated decision-making in setting up the general outline of the app.
During this stage, special attention was paid to the selection of the FCDB and the selection of the PSEA-aspects that both directly influence data accuracy21. Regarding the FCDB, as the app has originally been developed for use in the Netherlands, its food list is based on the Dutch FCDB, NEVO14. In the future, the aim is to further develop the app for international use, which requires more extensive food composition data as many foods are country-specific. Currently, no international FCDB exists yet and if existent, its use might have been limited. More specifically, as the Dutch food list already contains 2,389 food items, the implementation of an international food composition table, e.g., for 5 countries would probably multiply this number of food items by about 5 and negatively affect the searchability of foods and consequently, the app&#39;s usability. Therefore, country-specific food lists will probably be most valuable and often also preferred by professionals55.
This is facilitated by the app as it enables the import of alternative food lists and thus linkage to different (international) food composition tables. Regarding the portion sizes, there are multiple options available to support the accuracy of the estimates, e.g., use of image booklets, referent objects, and/or textual portion size suggestions26. In view of user-friendliness, direct implementation of a PSEA in the app is preferred over using a PSEA alongside the app (e.g., image booklet, referent objects). During the development of the app, the decision was made to facilitate portion size quantification by offering the opportunity to enter portion sizes using portion size suggestions and entry in grams. Portion size suggestion are based on the only available Dutch portion size database56. Although Dutch dietary assessment tools such as Compl-eat and Eetmeter also rely on this database13,17, it needs to be noted that this portion size database dates from 2003, and tableware sizes have since increased57. Using this database may therefore underestimate food intake.
Currently, the portion size database is being updated by the Dutch National Institute for Public Health and the Environment (RIVM), the Dutch Nutrition Center, and Wageningen University and Research58, which will eventually be used to update the portion size suggestions in the app. Discrepancies between the old and new portions will be mapped and adjusted where needed. Although the use of portion size images (i.e., a series of images portraying different amounts of a selected food) may be a good alternative for text-based portion size suggestions59, research has shown that the accuracy of portion size estimation is highest when a series of portion size images is presented at once, instead of one image at a time45,60,61. Generally, currently available smartphones have relatively small screens, which limits the presentation of a series of images. Although new technologies facilitate the use of interactive portion size graphics wherein amounts of food on a virtual plate or cup can be increased or decreased by using a slider61, these techniques are relatively new and still need to be thoroughly evaluated to assess their accuracy.
Another critical step in the development of the app included the involvement of experts and intended end-users. Although not often incorporated in the developmental process of tools (or not described)11,12, feedback from experts-as well as intended end-users-is crucial61, allows maximization of usability, and maintains the required level of accuracy. The feedback of the intended end-users was particularly helpful in the final design of the My Dishes function. Overall, the users were satisfied with the possibility to create their own dishes. However, they did struggle with some of the procedures, for instance, although the function would automatically save data, this was not visible to the user. Therefore, many users kept searching for the Save button and got stuck, afraid to go back and lose their input. Based on these kinds of feedback, the function was improved to better fit the expectations of the user.
To conclude, Traqq is an innovative app with many advantages over existing apps and web-based tools. However, there are still various limitations. As the app still relies on self-report, self-report-related measurement errors still exist (e.g., memory bias (i.e., in case of recall), social desirability bias, and food intake modifications (i.e., in case of food records), inaccurate portion size estimations (i.e., in both))1. In the coming years, recently launched novel technologies will be explored to further advance the app, e.g., by exploring the value of implementing features such as barcode scanners, voice recording, chatbots, and images, which could improve food identification and portion size estimation. Possibilities to connect with other apps (e.g., activity trackers, sleep trackers) and devices (e.g., accelerometers, heart rate monitors, chewing sensors) are being explored as well. Finally, the backend is also being subjected to further development e.g., through the expansion of sampling options.

Accurate dietary assessment is crucial to ensure the quality of studies on the role of nutrition in health and disease prevention. Currently, such studies generally use established self-report dietary assessment methods, i.e., food frequency questionnaires, 24-h recalls (24hRs), and/or food records1. Despite the fact that these methods are of major importance for nutrition research, they also possess various drawbacks, e.g., memory-related bias, social desirability bias, and are burdensome for the respondent as well as the researcher1,2. Recent technological inventions now offer the opportunity to overcome these drawbacks. During the past years, various research groups seized this opportunity and developed web-based and smartphone-based dietary assessment tools for nutrition research that address some of these known drawbacks (see Eldridge&#160;et al.3 for an extensive overview of web- and smartphone-based tools), i.e., reduce causes of error, improve user-friendliness, and decrease the participant&#39;s and researcher&#39;s burden1.
Nevertheless, the number of fully automated and validated smartphone applications (apps) that are appropriate for nutrition research is still limited. Most of the available dietary assessment apps (i.e., commercially or developed for research) are either not fully automated (i.e., require manual coding of food items) or are not (well) validated3. Moreover, most available validated apps have been developed for one specific research purpose and use in a specific country; due to rather fixed designs, re-using such apps for other research purposes or in other countries seems challenging3,4,5,6,7,8. Finally, despite the availability of food record-based apps, to date, no recall-based apps appear to exist yet. Although food records are prone to reactivity bias, i.e., respondents may alter their food intake due to the awareness that they are being observed2,9, this is not the case for recalls, which emphasizes the need for the development of a validated recall-based app10. An innovative dietary assessment app called Traqq was developed for use in the Netherlands which can be used as a food record as well as a recall, depending on the research question1.
Besides the possibility to alternate between the food record option and recall option, this app also differs from other dietary assessment tools because of its flexible nature. Specifically, regarding the food list, portion size estimates, sampling schemes, and the possibility to incorporate additional questions. The level of flexibility in the system enables tailoring to multiple research purposes that require accurate assessment of dietary behaviors. Currently, the app is in the process of being validated and will be ready to be used in various types of nutrition related research. The app can also be used, and perhaps further improved for use, in nutritional intervention programs to measure and influence dietary behaviors. As the development of reliable dietary assessment tools is challenging, and reports on these processes are scarce, especially with respect to user and expert involvement3,11,12, this paper provides a detailed overview on how different information sources were integrated in the systematic and iterative development of this smartphone-based dietary assessment app. The process incorporates theory, expert consultation, and user engagement.

<table><tbody><tr><td>ASA24 Portion size picture book</td><td>American National Cancer Institute</td><td>na</td><td>The portion size image database as used in the ASA24-tool</td></tr><tr><td>Atlas.ti v8</td><td>ATLAS.ti Scientific Software Development GmbH</td><td>na</td><td>Qualitative data analysis software for research</td></tr><tr><td>Compl-eat</td><td>Wageningen University</td><td>na</td><td>The portion size suggestions database as used in the Compl-eat 24hR module</td></tr><tr><td>iOS screen record function</td><td>Apple Inc.</td><td>na</td><td>Build-in iOS feature to make screen recordings</td></tr><tr><td>NEVO (version 2016/5.0)</td><td>RIVM</td><td>na</td><td>Dutch Food Composition Database</td></tr><tr><td>Qualtrics</td><td>Qualtrics XM</td><td>na</td><td>Online survey tool that can be used to implement additional questions in Traqq</td></tr><tr><td>Recordable</td><td>Invisibility ltd.</td><td>na</td><td>Android app to make screen recordings</td></tr><tr><td>SPSS version 24.0</td><td>IBM Corporation</td><td>na</td><td>Statistical software</td></tr><tr><td>System Usability Scale (SUS)</td><td>na</td><td>na</td><td>Validated questionnaire to assess a system&#039;s usability</td></tr></tbody></table>

iterative development of an innovative smartphone-based dietary assessment tool: traqq

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed (iOS/Android). This app can be used as a food record and&#160;24-h recall (or shorter recall periods). Different sampling schemes can be created on either prespecified or random days/times within a predetermined period for both methods, with&#160;push notifications to urge the participants to register their food intake. In case of non-response, notifications are automatically rescheduled to ensure complete data collection. For use as a food record, respondents can access the app and log their food intake throughout the day. Food records close automatically at the end of the day; recalls close after submission of the consumed items. The recall as well as the food record module provide access to an extensive food list based on the Dutch food composition database (FCDB), which can be accustomed to fit different research purposes. When selecting a food item, respondents are simultaneously prompted to insert portion size, i.e., in household measures (e.g., cups, spoons, glasses), standard portion sizes (e.g., small, medium, large), or weight in grams, and eating occasion/time of consumption. Portion size options can be adjusted, e.g., only entry in grams in case of a weighed food record or time of consumption instead of eating occasion). The app also includes a My Dishes function, which allows the respondent to create their own recipes or product combinations (e.g., a daily breakfast) and only report the total quantity consumed. Subsequently, the app accounts for yield and retention factors. The data are stored on a secure server. If desired, additional questions, i.e., in general or those related to specific food items or eating occasions can be incorporated. This paper describes the development of the system (app and backend), including expert evaluations and usability testing.

The system (app and backend) was developed using the steps outlined in the above described protocol; the key results of this process are described below, concluding with the final design of the app.
Formative research 
In addition to extensive literature review, several web-based tools were inspected (e.g., Compl-eat13, ASA2414, Foodbook2415, MyFood2416) with respect to dietary assessment methodology and implemented features. In addition, the performance of several food tracking apps frequently used in the Netherlands were compared (e.g., MijnEetmeter17, MyFitnessPal18, Virtuagym Food19), focusing on aspects such as dietary assessment methodology, provision of information, reliability, search engine, and the use of additional features (e.g., images, barcode scanner, recipe functions). The results of this inspection led to the decision to develop the app in such a way that it can be used as a food record and a recall. Moreover, it led to the implementation of the My Dishes function, which can be used to create original recipes or frequently consumed product combinations (e.g., a daily breakfast). Within this function, yield and retention factors are automatically taken into account.
To accurately quantify food and nutrient intake, a complete, albeit practical, food list is crucial. Compiling such a food list requires a trade-off between the extensiveness of the food list and the searchability of the food items (i.e., food descriptions need to be clear, understandable, and easy to locate)41,42. As food composition data form the fundamental basis for dietary assessment21,22, it is important to ensure that the developed food list can be linked to accurate food composition data. The food list included in the app is based on the Dutch FCDB (NEVO)14, which was selected for its reliability and rich food composition data. Originally, the NEVO consists of 2,389 food items (version 2016/5.0), which was reduced to a food list of 1,449 items by eliminating &#34;confusing items&#34; (e.g., foods that cannot be consumed raw, foods that cannot be consumed without additions) or items that are not as essential to include (e.g., due to low consumption rates based on the Dutch Food Consumption Survey (DNFCS)43).
Additionally, the NEVO contains similar foods with different brand names; in such a case, only the generic option was included in the food list. To further facilitate usability, some food items were renamed to eliminate needless terminology such as &#39;prepared&#39;, &#39;frozen&#39;, &#39;average&#39;, and &#39;natural&#39;. This &#34;cleaning protocol&#34; was developed by three well-trained research dietitians and executed by means of a syntax, which can be rerun once NEVO is updated. In addition, to optimize the searchability of food items, 1,019 well-known synonyms of the included foods were added to the food list. Thus, the food list included in the app eventually comprised 2,468 items. An overview of the food list development is displayed in Figure 2. To note, although this extensive food list has been developed for general use, the backend of the app does allow the import of alternative food lists if required.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62032/62032fig2v2.jpg" /> 
Figure 2: Structure of the food list developed for the app. The food list is based on the Dutch food composition database (FCDB) and corresponding portion size suggestions and synonyms were added for each item in the final food list. <a href="https://www.jove.com/files/ftp_upload/62032/62032fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Another crucial aspect of dietary assessment is the quantification of portion sizes. Although portion size estimation aids (PSEAs), e.g., images, referent objects, and standard portion sizes, support the reporting of the amounts of foods consumed24,26,44, misreport of portion sizes is still a substantial source of bias24,25,45,46, and literature on the effectiveness of the different PSEAs is inconsistent26. Food images, portion size suggestions (i.e., standard sizes and household measures), and free entry of weight in grams are the most used PSEAs in web- and smartphone-based dietary assessment tools34. For example, whereas portion size suggestions (e.g., cups, spoons, small, large) are used in tools such as Compl-eat13 and Oxford WebQ47, images aid portion size estimates in tools such as ASA2414 and Myfood2416. To investigate the most appropriate PSEA for the app, a pilot study was conducted to compare the accuracy of portion size suggestions (e.g., small, medium, large, or cup, spoon), free entry in grams, and portion size images. The results of this study led to the implementation of portion size suggestions as the PSEA in the app along with the option to enter amounts in grams27.
Expert review 
The aim of the expert evaluations was to qualitatively evaluate the app in terms of functionality and ease of learning. As many users prefer to learn software by exploration29, a system&#39;s level of learnability is important. A total of 10 experts, i.e., 4 (research) dietitians and 6 nutrition and health behavior experts (scientists) participated in the cognitive walkthroughs in which 60% used an Android smartphone. Most importantly, expert evaluations indicated that the first version of the app was not sufficiently intuitive, e.g., menu-structure was judged unclear due to vague buttons/icons, and the search engine generated an illogical order of results. Another critical point arising from the expert reviews related to the fact that selected items could not be modified. Based on these results, the design of the app was considerably upgraded from stage 2 onwards (Figure 1).
Usability evaluation 
A total of 22 participants participated in the think-aloud interviews, which formed the basis of the usability evaluation. The initial sample size was set at 20 participants36, after which data saturation was assessed. As data saturation was not reached after 20 interviews, inclusion continued while assessing data saturation after each successive interview. Participants had a mean &#177; standard deviation age of 48 &#177; 17 years (range 22-70 years); 36% were male, and the majority of the population was highly educated (55%). In addition, most participants used an Android device (n=14, 64%), and almost all participants had over 1 year of experience with smartphone use (n=21, 96%) (Table 1). All participants completed the tasks without or with minimal instruction.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Total (n=22)</td>
		</tr>
		<tr>
			<td>Gender</td>
			<td></td>
		</tr>
		<tr>
			<td>&#160;&#160; Male (%)</td>
			<td>36.4</td>
		</tr>
		<tr>
			<td>&#160;&#160; Female (%)</td>
			<td>63.6</td>
		</tr>
		<tr>
			<td>Mean age (mean, SD)</td>
			<td>48.1 (17.2)</td>
		</tr>
		<tr>
			<td>Educational level</td>
			<td></td>
		</tr>
		<tr>
			<td>&#160;&#160; Low (%)</td>
			<td>0</td>
		</tr>
		<tr>
			<td>&#160;&#160; Medium (%)</td>
			<td>45.5</td>
		</tr>
		<tr>
			<td>&#160;&#160; High (%)</td>
			<td>54.5</td>
		</tr>
		<tr>
			<td>Smartphone type</td>
			<td></td>
		</tr>
		<tr>
			<td>&#160;&#160; Android (%)</td>
			<td>63.6</td>
		</tr>
		<tr>
			<td>&#160;&#160; iOS (%)</td>
			<td>36.4</td>
		</tr>
		<tr>
			<td>Smartphone experience</td>
			<td></td>
		</tr>
		<tr>
			<td>&#160;&#160; Shorter than 6 months (%)</td>
			<td>4.5</td>
		</tr>
		<tr>
			<td>&#160;&#160; Between 6 months and 1 year (%)</td>
			<td>0</td>
		</tr>
		<tr>
			<td>&#160;&#160; Longer than 1 year (%)</td>
			<td>95.5</td>
		</tr>
		<tr>
			<td>SUS (mean, SD)</td>
			<td>79.4 (15.1)</td>
		</tr>
	</tbody>
</table>
Table 1. Characteristics of the study population and results of usability evaluation. Only the results of the system usability scale (SUS) are portrayed in this table along with the participant characteristics.
Whereas some participants (n=13, 59%) indicated difficulties while using the My Dishes functionality; others (n=5, 23%) encountered minor functionality issues such as slow response of the menu button and difficulties using buttons related to insufficient screen size of smaller smartphones). Moreover, 15 (68%) participants indicated their preference for an option to enter consumed portion sizes in grams. Finally, evaluation of the SUS score indicated a rating of 79/100 (range 40-100), wherein only 3 out of the 22 participants rated the app below 68/100 and 13 rated &#62;80/100, which suggests that the app can be considered user-friendly. Thus, overall, the suggested improvements were minor, and usability evaluations were promising. Subsequently, suggestions for improvement were discussed within the research team and, if deemed relevant, incorporated in the stage 4 upgrade to further optimize likability and usability of the app (Figure 1).
Final design 
The steps described in the protocol and the results of the evaluation study eventually resulted in a final design for the app and the backend, which aimed for a simple visual design. This app can be used as a food record and a recall. As described previously, the food list is a modified version of the NEVO. Portion size estimation is supported by food-specific portion size suggestions; consumed portions can also be entered in grams. In case of the recall version of the app, the researcher has the possibility to select different timeslots (e.g., 2hR, 8hR, or 24hR). To collect food intake data on different days and times, various sampling schemes can be created within a predetermined period. Push notifications invite respondents to record their food intake. To ensure complete data collection, invitations are automatically rescheduled in case of non-response. Within the recall module, respondents can only report their food intake after receiving an invitation. In case of the food record, respondents can access the app and log their food intake throughout the day.
In contrast to most 24hR tools, the recall module of the app is not based on the Automated Multiple-Pass Method-a five-step method for collecting food intake data for the previous 24 h48-as this method is too elaborate and time-consuming for use in an app. More specifically, to increase usability and enhance the compliance of the food intake recordings11,38,49, navigation was reduced to a minimum by limiting the number of screens that need to be accessed to 4 (Figure 3): 1) an Overview screen showing the reporting window; 2) consumed food items are reported through the Search screen, and once the desired item is selected 3) a dialog box appears probing eating occasion and consumed amount, after which 4) the user returns to the Overview screen now showing the recorded food items. In addition, the user could also use the My Dishes function to create recipes or product combinations, which can be entered via the Menu button.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62032/62032fig03.jpg" /> 
Figure 3: Schematic overview of the routing in the app. <a href="https://www.jove.com/files/ftp_upload/62032/62032fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The data are stored on a secure server. If desired, additional questions-general or related to specific eating occasions or food items-can be incorporated. The app can connect with online survey tools. Therefore, it is possible to conduct a survey unrelated to food intake via the app at prespecified times (e.g., context, behavioral, mood questions). It is also possible to ask specific questions related to reported food items or eating occasions (e.g., when apples are reported, when lunch is reported). The use of online survey tools provides an opportunity to ask many different questions via the app. The collected food intake data can be exported from the server and imported into nutrition calculation software for further analyses. In case of the use of additional questions, these data will be available in the survey tool as usual. The aim was to develop a well-structured and easy-to-use app. Some screenshots of the design can be seen in Figure 4A-E.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62032/62032fig04.jpg" /> 
Figure 4: Screenshots of the final version of the app. (A) The Start/Overview screen, showing the invitation with the (in this case) 2 h-recall period. The user can press Product toevoegen (i.e., Add item) to report a food item or Niets gegeten of gedronken (i.e., I did not eat or drink anything) in case nothing was consumed during this time window. (B) The Search screen, showing results matching the search term &#34;Jus&#34; from the food list. The desired item can be selected from the search results. (C) A pop-up screen requires input of details on the selected item &#34;Jus d &#39;orange&#34;. In this case, the app asks for the amount consumed and eating occasion. The user can go back to the search result by pressing Annuleren (i.e., cancel) or Opslaan (i.e., save) to go further. (D) The Overview again, this time showing all the reported items. Another item can be added (Product toevoegen) or the input can be sent (Lijst versturen). (E) After selecting Lijst versturen, a pop-up appears asking the user if they are sure that they want to send, and reminds the user that it is not possible to make any more changes after the list has been sent. The user has the option to cancel (Annuleren) or send (Versturen). <a href="https://www.jove.com/files/ftp_upload/62032/62032fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

This article describes the protocol for the development of an innovative smartphone-based dietary assessment application Traqq, including expert evaluations and usability testing.

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed ...

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4.5K Views.  Wageningen University and Research. This article describes the protocol for the development of an innovative smartphone-based dietary assessment application Traqq, including expert evaluations and usability testing.

Video: Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

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In session I, the test mouse is placed in the cage and its mobility is characterized by the number of transitions made between compartments. In session II, a stimulus mouse is placed under one of the inverted cups and the sociability of the test mouse is quantified by the amounts of time it spends near the cup containing the enclosed stimulus mouse vs. the empty inverted cup. In session III, the inverted cups are removed and both mice interact freely. The sociability of the test mouse in session III is quantified by the number of social approaches it makes toward the stimulus mouse and by the number of times it avoids a social approach by the stimulus mouse.
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<img alt="Figure 1" src="/files/ftp_upload/52508/52508fig1.jpg" /> 
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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

Control of Eating Behavior Using a Novel Feedback System

Subjects eat food from a plate that sits on a scale connected to a computer that records the weight loss of the plate during the meal and makes up a curve of food intake, meal duration and rate of eating modeled by a quadratic equation. The purpose of the method is to change eating behavior by providing visual feedback on the computer screen that the subject can adapt to because her/his own rate of eating appears on the screen during the meal. The data generated by the method is automatically analyzed and fitted to the quadratic equation using a custom made algorithm. The method has the advantage of recording eating behavior objectively and offers the possibility of changing eating behavior both in experiments and in clinical practice. A limitation may be that experimental subjects are affected by the method. The same limitation may be an advantage in clinical practice, as eating behavior is more easily stabilized by the method. A treatment that uses this method has normalized body weight and restored the health of several hundred patients with anorexia nervosa and other eating disorders and has reduced the weight and improved the health of severely overweight patients.

Subjects eat food from a plate placed on a scale connected to a computer that records the weight loss of the plate during the meal. Feedback on the computer screen allows the subject to adapt her/his eating behavior to reference curves thus normalizing body weight.

Subjects eat food from a plate that sits on a scale connected to a computer that records the weight loss of the plate during the meal and makes up a curve ...

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the Healthy Landscape and Healthy People Lab, National Taiwan University (HLHP-NTU), on smartphones and smartwatches to collect data on users' real-time psychological and physiological responses and environmental information. A flexible and integrated research method was proposed because it can be difficult to measure multi-dimensional aspects of personal data in on-site studies in landscape and outdoor recreation research. An on-site study conducted in 2020 at the National Taiwan University campus was used as an application example. A dataset of 385 participants was used after excluding invalid samples. During the experiment, participants were asked to walk around campus for 30 min when their heart rate and psychological-scale items were measured, together with several environmental metrics. This work aimed to provide a possible solution to help on-site studies track real-time human responses that match ambient factors. Due to the app's flexibility, its use on wearable devices shows excellent potential for multidisciplinary research studies.

The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the ...

Environment

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. Recent technological innovations allow for the development of more accurate and precise dietary and eating behavior assessment tools that require less effort for both the user and the researcher. Therefore, a new sensor-based device to assess food intake and eating behavior was developed. The device is a regular dining tray equipped with a video camera and three separate built-in weighing stations. The weighing stations measure the weight of the bowl, plate, and drinking cup continuously over the course of a meal. The video camera positioned to the face records eating behavior characteristics (chews, bites), which are analyzed using artificial intelligence (AI)-based automatic facial expression software. The tray weight and the video data are transported at real-time to a personal computer (PC) using a wireless receiver. The outcomes of interest, such as the amount eaten, eating rate and bite size, can be calculated by subtracting the data of these measures at the timepoints of interest. The information obtained by the current version of the tray can be used for research purposes, an upgraded version of the device would also facilitate the provision of more personalized advice on dietary intake and eating behavior. Contrary to the conventional dietary assessment methods, this dietary assessment device measures food intake directly within a meal and is not dependent on memory or the portion size estimation. Ultimately, this device is therefore suited for daily main meal food intake and eating behavior measures. In the future, this technology based dietary assessment method can be linked to health applications or smart watches to obtain a complete overview of exercise, energy intake, and eating behavior.

This protocol shows and explains a new technology-based dietary assessment method. The method consists of a dining tray with multiple built-in weighing scales and a video camera. The device is unique in the sense that it incorporates automated measures of food and drink intake and eating behavior over the course of a meal.

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. ...

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Eating disorders (anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified eating or feeding disorders) have a combined prevalence of 13% and are associated with severe physical and psychosocial problems. Early diagnosis, which is important for effective treatment and prevention of undesirable long-term health consequences, imposes problems among non-specialist clinicians unfamiliar with these patients, such as those working in primary care. Early, accurate diagnosis, particularly in primary care, allows expert interventions early enough in the disorder to facilitate positive treatment outcomes. Computer-assisted diagnostic procedures offer a possible solution to this problem by providing expertise via an algorithm that has been developed from a large number of cases that have been diagnosed in person by expert diagnosticians and expert caregivers. A web-based system for determining an accurate diagnosis for patients suspected to suffer from an eating disorder was developed based on these data. The process is automated using an algorithm that estimates the respondent's probability of having an eating disorder and the type of eating disorder the individual has. The system provides a report that works as an aid for clinicians during the diagnostic process and serves as an educational tool for new clinicians.

Diagnosing eating disorders in healthcare is challenging. Therefore, the present protocol develops an algorithm based on 949 patient responses to a questionnaire, with the diagnosis displayed on an easy-to-use web-based interface. This system facilitates the accurate diagnosis of eating disorders while excluding those believed to have an eating disorder.

Eating disorders (anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified eating or feeding disorders) have a combined prevalence of ...

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Different methods can be used in research to assess dietary intake, many of which are still paper-based. Written estimated food diaries are often utilized in clinical trials, despite being a burden for both study participants and researchers. This method requires participant literacy, it is time consuming, labor intensive, and can easily lead to under-reporting. With advancements in technology, there is a growing interest in electronic diaries that automate the dietary assessment process. These are focused on improving accuracy, reducing both time and cost and providing users with a visual and more enjoyable experience. The methodology presented here aimed to validate the &#39;Boden Food Plate&#39;, a novel web-based platform for self-recording of food and drink items, compared to a traditional estimated food diary. The application was also rated on a satisfaction scale by study participants using a paper-based questionnaire. Sixty-seven participants completed the dietary measures on both the three-day electronic and paper food diaries. For the analysis, only dietary data completed at both study time points (baseline and week six) was utilized. Despite small mean differences between dietary data collection methods, Bland Altman analysis showed fairly wide 95% limits of agreement between the electronic platform and the written estimated food diary and there were few cases which did not fall within the 95% confidence intervals. Overall, participants found the electronic food diary to be more fun than the paper method and as easy to use as hard copy diaries. The new platform has potential as a self-recording tool for the collection of dietary data, particularly when utilized in clinical trial settings. However, further validation studies are needed to improve the validity of this novel electronic dietary data collection tool.

The &#39;Boden Food Plate&#39; is an electronic food diary designed to be an interactive and fun method to collect dietary intake using visual depictions. The purpose of this study was to validate the web-based application against a traditional three-day estimated food diary method.

Different methods can be used in research to assess dietary intake, many of which are still paper-based. Written estimated food diaries are often utilized ...

Medicine

Mobile Device-assisted Dietary Ecological Momentary Assessments for the Evaluation of the Adherence to the Mediterranean Diet in a Continuous Manner

Mobile device-assisted dietary ecological momentary assessments (EMAs) have emerged as a new tool allowing the evaluation of dietary intake in real time, in a real-world setting and in a continuous manner. They have the potential to minimize recall bias, participant, and investigator burden, and decrease economic and time investment while maximizing ecological validity.
We developed a set of EMAs aimed at evaluating continuous adherence to the MedDiet. Four multiple-choice EMAs are sent daily in a randomized manner from a total of eight questions. The EMAs enquire about the consumption of 11 key food groups of the Mediterranean diet in the last 24-48 h in a semi-quantitative way. EMAs capture the daily frequency of consumption of fruits, vegetables, and extra virgin olive oil on different days of the week. Additionally, EMAs capture the weekly frequency of consumption of whole grain products, sugary drinks, nuts, legumes, sweets, fish and seafood, and red and processed meats. A designed scoring system behind the EMAs extracts the percentage of adherence to the MedDiet recommendations and calculates a quality index of the diet every week. Individualized reports are sent periodically to the volunteers highlighting the strengths and weaknesses of their diet. EMAs are also expected to have a behavioral effect, reinforcing the choice of Mediterranean foods.

Presented here is a study protocol aimed at monitoring continuous adherence to the Mediterranean diet (MedDiet) by means of ecological momentary assessments. The method evaluates the intake of key food groups of the MedDiet and calculates an index of adherence.

Mobile device-assisted dietary ecological momentary assessments (EMAs) have emerged as a new tool allowing the evaluation of dietary intake in real time, ...

Integrated Spreadsheets for Nutritional Analysis of Population Diet Surveys

It can be challenging to calculate nutrient intakes in population diet surveys because existing nutritional analysis software is generally oriented toward analyzing intakes of individuals and may not allow users to input or easily modify the food composition data used in the analysis. These are drawbacks that are more problematic in low- and middle-income country settings. While there are numerous software-assisted dietary assessment platforms that conduct onboard nutritional analysis and are appropriate for use in large surveys, they are often similarly limited, and further restrict users to specific assessment modalities. This paper presents a multifunctional system of integrated spreadsheets for nutritional analysis of population diet surveys (ISNAPDS) that provides a solution for situations in which data have been collected but cannot be adequately analyzed with existing software. The protocol involves supplying the system with fully customizable data on food composition, food group classifications, and food intake (food intake in g/day may be entered directly or calculated based on user-supplied intake frequencies and either standard or variable serving sizes). Following data entry, the user modifies a set of simple pre-populated formulas to match them to the structure of the input data and the system applies these formulas to calculate nutrient and food group intakes, and the contributions of food groups to nutrient intakes for all members of the survey population. The flexibility of the ISNAPDS system allows it to accommodate the global diversity of foods consumed and analyze quantitative, semiquantitative, and nonquantitative food consumption data collected using prospective and retrospective assessment methods employing different reference periods and portion size estimation methods. To date, the system has been applied in published and ongoing analyses of 24 h recall, diet record, food frequency, and disaggregated household consumption data from population surveys in China, Ethiopia, India, Mongolia, Thailand, and a multi-country analysis of 10 sub-Saharan African countries.

This paper presents a system of integrated spreadsheets using simple formulas to calculate nutrient and food group intakes and the contributions of food groups to nutrient intakes for analysis of population diet survey data. The system accommodates quantitative, semiquantitative, and nonquantitative food intake data and a user-supplied food composition table.

It can be challenging to calculate nutrient intakes in population diet surveys because existing nutritional analysis software is generally oriented toward ...