Development Of Nutrition And Physical Activity Behavior

Published Date: 02 Nov 2017

Abstract

Background: Young adults (aged 18 to 35) are a population group at high risk for weight gain, yet we know little about how

to intervene in this group. Easy access to treatment and support with self-monitoring of their behaviors may be important.

Smartphones are gaining in popularity with this population group and software applications ("apps") used on these mobile devices

are a novel technology that can be used to deliver brief health behavior change interventions directly to individuals en masse,

with potentially favorable cost-utility. However, existing apps for modifying nutrition or physical activity behaviors may not

always reflect best practice guidelines for weight management.

Objective: This paper describes the process of developing four apps aimed at modifying key lifestyle behaviors associated with

weight gain during young adulthood, including physical activity, and consumption of take-out foods (fast food), fruit and vegetables,

and sugar-sweetened drinks.

Methods: The development process involved: (1) deciding on the behavior change strategies, relevant guidelines, graphic

design, and potential data collection; (2) selecting the platform (Web-based versus native); (3) creating the design, which required

decisions about the user interface, architecture of the relational database, and programming code; and (4) testing the prototype

versions with the target audience (young adults aged 18 to 35).

Results: The four apps took 18 months to develop, involving the fields of marketing, nutrition and dietetics, physical activity,

and information technology. Ten subjects provided qualitative feedback about using the apps. The slow running speed of the apps

(due to a reliance on an active Internet connection) was the primary issue identified by this group, as well as the requirement to

log in to the apps.

Conclusions: Smartphone apps may be an innovative medium for delivering individual health behavior change intervention en

masse, but researchers must give consideration to the target population, available technologies, existing commercial apps, and

the possibility that their use will be irregular and short-lived.

(JMIR Res Protoc 2012;1(2):e9) doi:10.2196/resprot.2205

KEYWORDS

cellular phone; young adult; primary prevention; lifestyle; health behavior

Introduction:

Around the world the average number of mobile phone user is 1.18 and day by day this number is increasing [1]. Globally in 2011 the number of Smartphone user is approximately 490 million compared to approximately 300 million in 2010 [2]. In USA 62 percent Smartphone users aged in between 25 to 34, whereas in 2010 this number is 41 percent [6]. From the public health prospective Smartphone can play a potential rule and which is cost effective. Beside this now a day’s researchers have largely developed apps to mediate in the clinical care setting for patient self-management. Whereby a patient monitors themselves and receives curative feedback, or for real-time treatment where no self-reported data is required from the patient. Commercial sector has developed numerous informatics apps for weight loss like nutrition and physical activity. Although most of them are based on calorie counting approaches and may not always provide best practice plan for weight management [4]. In most Europeans countries, young group of population are at high risk for becoming overweight or obese. According to the US Coronary Artery Risk Development in Young Adults (CARDIA) cohort, on an average females gained 0.7 kg and males gained 0.8 kg, each year [7]. Weight of this population group is increasing due to four key lifestyle behaviors. These behaviors comprise a turn down in physical activity, taking excessive fast food [8], over-consumption of sweetmeat [3], and insufficient consumption of fruit and vegetables. On the other hand, there is partial proof to notify what method might be effective for avoid weight gain in this group [5], although normal treatment and providing support for development and self-monitoring actions may be important. Hence, we get on building a series of Smartphone apps that assist young adults in building healthy lifestyle habits. This paper explains the procedure of developing four separate Smartphone apps and talk about our insights from this process.

Related Work:

People of 18 to 35 years are at high risk for weight gain and we identify little about how to intervene in this group. It is important to provide them proper treatment and support them with self monitoring of their behaviors. Smartphone are becoming popular to young aged people day by day and the applications used in this device can play an important role to change health behavior and it is cost effective. However, present apps for adapting nutrition or physical activity behaviors may not always suggest best practice guidelines for weight management.

Methods:

The development methods consisted of four stages: (1) deciding on the specifications, (2) selecting the platform, (3) creating the design, and (4) testing the prototypes.

1: Deciding on the Specifications:

The first phase engages defining the principle of each application. This entail state the related public health rule to notify the objective for actions change, the definite policies for actions change, the images or realistic design, and the prospective data to be composed. The basic reason of the application was to bear change in the lifestyle activities acknowledged. To support young adults with recover their food habits, rules for these actions had to be distinct to create rules about what is sufficient. Related public health policy was talk about for physical activity levels, eating of fruit and vegetables, and suggests limits for remove meals and sugar-sweetened drinks. For example, the physical activity application (ePASS) incorporated a "good health" target of 30 minutes of reasonable level of physical activity every day based on the World Health Organization’s physical action rule of at least 150 minutes of aerobic activity per week for adults, and a "healthy weight" target of 60 minutes of reasonable level physical activity every day based on expert consent that up to 60 minutes of reasonable action daily is necessary to avoid harmful weight gain. The fruit and vegetable application (eVIP) afford users with a graphical presentation of the amount of fruit and vegetable portion they documentation out of the two portion of fruit and five portion of vegetables suggested by the Australian Government Department of Health and Aging. The sugar-sweetened drinks application (eSIYP) offered users with a color exhibit of their total power, sugar, and alcohol intake from all drinks consumed, in which green, orange, and red point out the "perfect," "satisfactory," and "too much" threshold levels of intake, correspondingly. These threshold levels were stand on nutritional specialist judgment of the suggested limits on utilization of added sugars, as per the Australian Dietary Guidelines. The take-out food application (eTIYP) also offered users with a color exhibit of the standard energy and fat satisfied of take-out meals, in which green indicated satisfactory intake and red indicated extreme intake, compare to ≤30% and >30% of the nutritional intake suggested by the Australian National Health and Medical Research Council and the New Zealand Ministry of Health, correspondingly, according to age and gender. In terms of behavior change policy, young adults frequently not have the self-regulatory ability, such as self-monitoring and preparation, obligatory to accept and continue healthy behaviors. Self-regulation was promoted in each application by offering users with a stage to make every day entries of their behavior (eg, physical activities execute or vegetables eaten) from which they were offered daily or weekly précis of their stated behavior, in mention to public health strategy, to allow their supervising of and forecast around these behaviors. Presenting advice that is individually related may also is a significant policy for shifting young adults’ behaviors. Support from achievement of accomplishing an objective and social influence can improve self-efficacy, and has been recognized as significant in attaining behavior change, mainly for the period of active or tense condition. All application offer motivational tips as a basis of optimistic support that would help the young adults in making more optimistic attitude around their capability to change their behavior (eg, "You can rip up your exercise goal into as little as 15-minute rupture"). These tips were also customized to users’ self-reported behavior. For example, if a user’s informing physical action did not meet suggested rule, they were shown a related motivational tip (ie, "Plan use in advance and write it down if you can—try phone reminders"). Reflection was also given to the graphic plan and how this might sway behavior. The behavior-image model propose that through procedure of social- and self-comparison, persons will compare themselves to comparable human images and create protrusion of themselves have the preferred distinctiveness of the humans in those images. This procedure is referred to as "self-reevaluation" in the Trantheoretical Model, which uses fit role models and descriptions to assist ones succession from considering behavior change to arranging for changing behavior. Hence, in the application we used descriptions of young adults who were performing the target behaviors (eg, riding a bike or drinking water) and possessing attractive distinctiveness of a normal healthy look and existence, to inspire users toward changing the target behaviors. Likewise, we displayed better foods and drinks, rather than "rubbish" foods, to model these foods as ideal for use. All images were buying from a profitable graphics corporation to avoid probable break in copyright. To help future investigate, we allow the following data items to be exported: user recognition (ID), log-in ID, sex, age, and the date and time of log-ins into each application. Supplementary information that was capable to be exported from each app included: physical activities execute and their period; drinks obsessive and their volume, total sugar, alcohol, and power content; energy and fat content of take-out meals, the restaurant where the meal was consumed, and the contents of the meal; and the number of servings and types of fruit and vegetables consumed. This data could be exported into comma-separated value files from our relational database (described later), which could then be exported into statistical software for further analysis.

2: Selecting the Platform:

Usually, applications are building up for one definite operating system (native app), such as iOS, Android, Windows, Symbian, and BlackBerry, or build up as Web-based apps. Native application run locally on a smartphone’s operating system in a way that is equivalent to programs running on a desktop computer. Web-based application run like a Web page, whereby the application functions on an outside server and the user admission the application through the Web browser on their mobile device. Due to this efficient dissimilarity, Web-based apps may be used on all smartphones in spite of the operating system as long as the user has Internet access; however, there is less chance to operate the accessible hardware built into the phone (eg, the camera, geopositioning, or calendar). In our case, we improvised Web-based application because we did not need the use of accessible hardware on the phone and we required to facilitate downloading of data recorded by users and allow the application to be used on numerous operating systems and throughout the Internet for those who did not own a smartphone. However, Web-based applications are gradually becoming able to achieve like native application with an offline mode that can be contact without Internet connectivity. Correspondingly, there is growing probability for local application to have Internet connectivity, allowing the user to download updates and data from the user to be uploaded to a server. This highlight the lively nature of mobile technologies and why the type of stage selected should be talk about with an information technology expert.

3: Creating the Design:

To allow users to record their behavior, each application had to be connected with the related data for that behavior. The following data items were built-in for each application:

1. ePASS: type of activity (ie, gym, games, entertaining, or housework) and the concentration (ie, reasonable vs dynamic) of 91 unique activities, where "sensible" was defined as a metabolic equal of task (MET) value of 3-6 and "dynamic" was defined as > 6, derived from the compendium of physical activities.

2. eVIP: serving size equal for 48 types of fruit and 61 vegetables, where 1 serving was equivalent to 150 g of fruit or 75 g of vegetables (eg, 0.5 cups cut or 4 parts of cooked asparagus are both equal to 1 serving of vegetables).

3. eTIYP: total power and fat content of 504 take-out food and drink menu items.

4. eSIYP: drink category (eg, waters, vitamin waters, hot chocolate, tea/coffee, alcohol, soft drinks, sports drinks, cordials, fruit juices, fruit drinks, flavored milks, and milkshakes) and the total power, sugar, and alcohol content of 114 unique drinks. Nutrient composition data for eTIYP and eSIYP were basis from the Australian government food and nutrient database, NUTTAB. The foods scheduled in this database were chemically examine or, when unavailable, sourced from food producer nutrient label data which may or may not be based on chemical examination. These data items were all contained within one relational database, which is fundamentally a database where the data items (or variables) are managed into a sequence of tables with each table, characterize a dissimilar feature or "relation" in the data. For each application, there was 1-2 tables hold the behavioral data. For example, the eVIP application need one table for the types of fruit and vegetables, such as "eggplant/aubergine (cooked)," and a second table listing the piece sizes, such as "0.5 cups diced," "3 thin slices," or "1 thick slice," with their individual serving equal of 1 serving, 1 serving and 0.5 servings. An extra table was built-in for each application containing the promossional tips and another table holds user details (ie, name, age, gender, log-in ID, and user ID). The relational database management system software, MySQL (Oracle Corporation, Redwood Shores, California, United States), was used to admission and inquiry data items limited within the relational database using planned query language (SQL). For example, if a user logged into the eVIP application at lunchtime, SQL was used to recognize a motivational tip about including fruit or vegetables at the lunch meal to present to the user from the motivational tips table in the relational database for the eVIP application. Programming for the application was written with Python programming language software (Python Software Foundation, Wolfeboro Falls, New Hampshire, United States) to converse with the relational database and make the user interface (ie, what the user sees and cooperate with), counting the HyperText Markup Language (HTML) (ie, the building blocks of a Web page) and the cascading style sheets (CSS) (ie, the visual formatting of the HTML). The HTML and CSS information was then understand by the Web browser on the user’s mobile device to create the user interface. To exemplify an example of this programming, if a user recorded 0.5 helping of fruit, this data was stored in the relational database and the user was immediately accessible with a picture of half of one application shaded green (representing 0.5 servings of fruit) on the home screen of the eVIP application. An individual application was created for each of the four behaviors, rather than creating a joint application, to authorize the targeting of diverse behaviors in future interference research, such as addressing only those behaviors that are mainly difficult for the individual. Further, there was a need to bound each application to < 6 screens, five of which were general to all of the application to simplify and boost the speed at which users could steer their way through the application.

4: Testing the Prototypes:

Data obtainable and influence in the sample edition of each application were crosschecked adjacent to the relational database by two authors (LH and AC) for correctness. This involved recording intake and activity behaviors at arbitrary in each of the four application and examine whether the data accessible to the users were accurate (eg, the total power of a take-out food item) and that the data were exactly influenced in the application (eg, change fruit and vegetable piece recorded into the number of equal servings or summing the total energy content of all sugar-sweetened drinks recorded). Twenty-one adults aged 18 to 35 years who were participating in a weight loss trial were offered access to the application, and were asked for their view on the presentation of the application as part of an online assessment. This assessment included two questions evaluate the usability of the application, including: "Did you have any troubles downloading the smartphone applications?" and "Did you have any troubles using the smartphone applications?" with three reply options: "yes," "no," or "did not access them." If they reply "yes," they were then encouraged in an open-ended question: "Please tell us what troubles you knowledgeable." Two other open-ended questions asked, "How could the smartphone apps be better?" and "Please tell us any other clarification that you have." Re-occurring themes in the qualitative reply to these open-ended questions were recognized and sum up. Actions for gathering this information were accepted by the University of Sydney Human Research Ethics Committee (approval #13698).

Results:

The applications took 18 months to construct as well as formation of the relational databases, discover behavior change policy, reviewing study with young adults on the key behaviors, creating the plans with information technology support, and testing the sample. Once the first application was developed, the others took fewer times with the physical activity application developed most quickly. The development concerned the fields of marketing, nutrition and dietetics, physical activity, and information technology. The cost of structure all of the information into an application was approximately US $5000 per applications—less than half the cost of typical commercial firms because we engaged Information Technology students for the development. The four applications were establish to return the accurate data to the user from the relational database and computation execute by the applications were correct (eg, compute the energy, sugar, and alcohol content in 390 mL of a drink recorded by the user). The same generic user line (ie, what the user sees and interacts with) was used in all four applications. Of the 21 member presented to use these applications, only 10 assess them. These members reported no obscurity with downloading the applications. Overall, participants did not like having to log in to use the applications. Some member find fault that the applications running slowly on their mobile devices (eg, "The smart phone application was a good idea but as it was a Web application, it often solidify and it was a bit slow in broad and scrolling lists were not useful on some operating systems" [from a 19-year-old female] and "Some parts of the applications didn’t work for me, such as the scroll, so I couldn’t enter many fruits/veg" [from a 22-year-old female]). Only one respondent (33-year-old female) offered a recommended development: "Applications could be planned to reward/monitor good behaviors only..."

Discussion:

In this paper, we have illustrated the method of improving four smartphone applications meant at improving nutrition and physical activity life behaviors during young adulthood. The applications were establish to display exact data and to manipulate data exactly for the user. A small sample of young adults offered qualitative advice. It was establish that the slow running speed of the applications (because of their reliance on an active Internet connection) was an issue for the target consultation (young adults aged 18 to 35), as was the necessity to log in to the applications. There was little proposal for modify in the information offered or graphics used in the applications, though most of the open-ended questions were posed to ask about matter or troubles with the applications so that it produce negative rather than optimistic comment. It is also recognized that the small sample size of members testing the applications restrictions the authority of these result. Very few researchers in public health have reported on the development and use of smartphone applications for personal dietary or physical activity change. Mattila et al used a wellness diary for recording self-management of weight-related behaviors, Hughes et al developed an application for observing power balance, Lee et al developed a weight loss diet game, while others have observed diet or physical activity as part of a program for diabetes or cardiac rehabilitation. The uptake and usage of these applications has been reasonable to high among adults in the involvement setting. Smartphone applications have the probable to advance population health, largely because of their extensive and growing use, energetic technological improvement, capability to download updates, and use of existing features (eg, Internet access, geopositioning technology, as well as photo, video, and voice recording capabilities), and the possibility for dipping involvement delivery costs. However, there are boundaries to the use of smartphone applications, primarily because they may be costly to develop and their use is often uneven and short-lived. Therefore, if the target behaviors need promise in the longer term, as is the case with nutrition and physical activity behaviors, extra strategies may be necessary to extend personals’ incentive to use the applications. Also to consider, is the opposition from new applications being developed. For this reason, an audit of existing applications is suggested to inform whether sufficient applications already exist. An additional limitation is that other difficulty to nutritional and physical activity behavior change apparent by young adults cannot be addressed, such as financial costs and aspects of their social and physical environments, although one can address personal barriers, such as time constraints or lack of self-monitoring skills. The equality of using apps as a public health strategy also remains questionable, and is likely to depend on the specific target group of interest. Although young adults are increasingly using smartphones, use in other population groups is unclear. For this reason, formative research with the target population may be required for some groups, such as older adults, before embarking on developing apps for this demographic. Future research should also examine how commercially developed apps for diet or physical activity are being used by different population groups to improve our understanding of how this technology may be used to support behavior change. The feedback from trialing the apps with the target population (young adults aged 18 to 35) will be used to refine the prototype versions of the developed smartphone apps. Attempts will be made to increase the speed of the apps and ensure functionality on all mobile phone operating systems popular with young adults. The revised apps will then be formally tested for their "usability," which measures the ability of a software product to be understood, learned, used, and be attractive to the user, and will involve an analysis of the number of steps or time required to complete set tasks within the software. Others have extended these methods to testing the usability of mobile phone apps, suggesting additional items for evaluation. The apps will be added as part of a multi-component randomized controlled trial in young adults, together with mobile phone text messaging and phone coaching calls, to evaluate their impact using validated measures of diet, physical activity, and anthropometrics. Smartphone apps may be an innovative medium for delivering individual health behavior change intervention en masse. Researchers or health professionals considering developing an app in their area must give careful consideration to the target population in terms of their access, ability to adopt this form of intervention, and preferences regarding the design, the current technologies available for app development, existing commercial apps, and the possibility that their use will be unequal and brief.