Ease of access and user friendliness make diet-tracking apps an important ally in their users’ efforts to lose and manage weight. To foster motivation for long-term use and to achieve goals, it is necessary to better understand users’ opinions and needs for dietary self-monitoring. Motivating users to use an app over time could help them better achieve their nutrition goals.
Overall Ratings
Cronometer is all about that tracking, and they recently came out with recurring food and custom “meals” (in addition recipes and foods) that are great. I’m on a 122 day streak with it and no longer get hangry, have my weight right where I want it, and hit my macros each day! Definitely worth reading their post on data sources to understand the different options for recording; I’m a fan of just finding the closest item to what I’m eating in NCCDB and then getting all the correct data. A calorie counting app that helps people reach their weight loss goal. To get started just input your profile details with your goal weight and we’ll calculate the daily calorie budget best for you. Next, easily track your food, weight, and activity and get ready to celebrate your weight-loss victories.
Free Nutrition Apps
A total of 72,084 user reviews in English were identified in this step using the Python library langdetect. Every mini-course will help you gain specific knowledge, tools, and skills that will help you change your habits, lose weight, and make progress far beyond the scale. You can make additional in-app purchases that range from $4.99 to $89.99.
How much money are Ronda Rousey and Gina Carano getting paid to fight on Netflix?
Speed, reproducibility, and reliability are considered some of the most important advantages of text mining when it comes to classifying and categorizing text [39]. Obesity and overweight are the result of a plethora of environmental factors that are known to influence individuals’ food intake and physical activity [1,2]. As obesity is a global public health challenge that leads to numerous health, social, and economic difficulties [3], it is important to help individuals make better and healthier dietary choices. It worked don’t get me wrong but it was very advertisey and pushy.
As consumers increasingly rely on apps to support their daily activities, they also generate invaluable feedback for both developers and potential users through app reviews and ratings. These reviews typically contain information that is valuable for app evaluation, including user opinions about the app, information about their experiences with the app, and bug complaints or feature suggestions [22]. A previous study showed that almost a quarter (23.3%) of app reviews contain an app feature request or app assessment [23]. In our study, we focused on the user perspective, and aimed to evaluate the diet-tracking apps and their features that are most frequently commented on by users in app reviews.
Links to NCBI Databases
- Foodnoms is available on iPhone, iPad, Mac, and Apple Watch with an optional paid subscription.
- Top 50 “positive” trigrams (most frequently mentioned trigrams with ratings over 4).
- These reviews make up about 12% of our dataset (9576 reviews), and it was safer to remove them than to translate them into English.
- These two trigrams would then be added to the trigrams extracted from other reviews, resulting in a total of 744,808 trigrams from 72,084 reviews.
- Second, lemmatization was performed, which is a process of grouping the inflected forms of words so that they can be analyzed as a single item.
- Previous research has mainly focused on app development issues and feature evaluation to make apps more accessible and user-friendly (eg, [7,31]).
This library helped us to build a mathematical model that could classify each review by topic. The list of possible topics was determined during model training, and we predetermined the number of possible topics. To find the most appropriate number of topics, we used the coherence score. Topic coherence measures the degree of semantic similarity between the highly scored words in the topic, which can help to distinguish between topics that are semantically interpretable and topics that are artifacts of statistical inference [49]. This value is given after each model training process and helped us determine the performance of our trained model. After data preprocessing, a new dataset was obtained with cleaned data that could be used for both topic modeling and n-grams identification.
Topic Selection Process
With the increase in publicly available user-generated content due to the proliferation of internet-assisted communication, researchers have developed several automated approaches to identify, summarize, and classify the available information [26,36]. The development of new tools allows researchers to obtain more information about users’ opinions and sentiments in their writing. There is a trend to shift the focus of opinion mining from studying long texts to shorter user posts on various social media platforms and websites [22].
Finding the best number of topics that would give optimal results required several trials, starting with a randomly selected number of topics until we narrowed down to the model with the best score. For example, if our model found 11 topics in the dataset, for each review in our dataset, the model would provide us with the probabilities of how likely the review is to belong to each of the 11 topics. After using MFP for many years, I recently found Lose It to be much easier to use on a day to day basis as far as data entry and scanning labels.
We then converted the text to lowercase, performed an extensive spell check of every review, and made necessary corrections using the Speller Python library. Words such as “I,” “are,” “and,” and “the” were considered “stop words” and removed, as such common words tend to dominate the results. We further removed any special characters and numbers from the reviews. This Information Guide may contain information and/or instructional materials developed by Michigan Medicine for the typical patient with your condition.
Methods
I’ve never figured out how it uses the is unimeal legit integration with Garmin to “add calories to the day” because you did a big ride. It eventually led to me just not tracking even though I paid for membership. It has a very good database and tracking functionality for macros. It also has diet plans and recipes that I’ve actually found useful. One thing that took some getting used to is that Garmin doesn’t sync over the calories from a workout, instead it syncs Garmin Active Calories and compares it to the you calorie budget. I suppose this makes more sense as it more accurately accounts for nonexercise time as well.
Healthy eating.
The number of mentions of positive and negative trigrams in user reviews also showed a trend of positive evaluation dominance among users leaving reviews. The top 50 most frequent positive trigrams appeared 12,723 times, while the top 50 most frequent negative trigrams were mentioned 1270 times in our dataset of 72,084 user reviews. Although this study provides valuable insight into user opinions, it is not without limitations. Owing to feasibility constraints, we focused on available reviews and introduced a set of constraints that allowed us to structure and summarize the otherwise diverse user-generated content in the form of app reviews. Future research could apply other text-mining approaches for data collection, cleaning, and analysis. In performing similar studies, it may be beneficial to differentiate users and their motivations for using the diet-tracking app.
The low inclusion of behavior change strategies in diet tracking apps may hinder their ability to help users achieve their long-term diet and nutrition goals [11,12]. However, diet-tracking apps that successfully employ behavior change strategies can have a positive effect on their users’ motivation, habits, and diet and nutrition outcomes [13-16]. These apps have also proven to be helpful in behavioral control and weight management [15].
This text can then be used to predict and understand user preferences and behaviors [26]. The aim of this study was to identify the key topics and issues that users highlight in their reviews of diet-tracking apps on Google Play Store. In addition, only apps that had the highest download numbers in the market were selected for this study.
GitHub – davidhealey/waistline: Libre calorie counter app for Android. Built…
And then found out that NO MATTER what, you can’t get your money back once they have charged the subscription even if it’s on THE SAME DAY. I get that they are here to make money, but seriously, just throw a nonintrusive ad window in somewhere and don’t pester me. Not currently tracking but am now off the bike with an injury so i might start again soon.
Topic Modeling
We collected 72,084 user reviews from Google Play Store for 15 diet-tracking apps that allow users to track and count calories. After a series of text processing operations, two text-mining techniques (topic modeling and topical n-grams) were applied to the corpus of user reviews of diet-tracking apps. Topic modeling is another text-mining and NLP method that is commonly used to discover latent topics in a corpus of text. Topic modeling has been shown to be useful for clustering documents or text, and is considered a probabilistic statistical technique for semantic structures [48]. In this study, we used the Python Gensim library, which is commonly used in NLP, for topic modeling analysis.