We singled out Reddit from other social media platforms due to its large user base, comment ranking system, mode of posting (questions and answers within subject matter “communities,” self-disclosure), and researcher-friendly API. The process we undertook to identify and extract data from posts on social media was to first decide upon keywords related to PA and motivation based on an initial search to identify relevant communities. Then, the next step was to identify relevant conversations within the selected subreddits. We will now elaborate on each step of this process:

We ran a Google search of Reddit using the keywords “motivation” and “exercise” or “sport.” This search returned 59 communities (ie, subreddits).

We began by excluding subreddits containing fewer than 100,000 users (19 groups) and non-English language groups (2 groups). Following initial examination of the remaining subreddits, we found that they could be broadly divided into 2 categories: “general” (25 groups) and “sports-related” (13 groups). Within these 2 categories, we observed that some subreddits were not topically related to our query (motivation in exercise and sport), but rather, they were geared toward other topics (eg, science, GARMIN, and sports leagues such as the NBA [National Basketball Association] and FIFA [Fédération Internationale de Football Association]) and were therefore excluded (17 and 5 from general and sport-related, respectively). Following further examination of the content of the conversations in the remaining sports-related subreddits (bodybuilding, judo, running), we observed that most of the discussions were of a technical nature (ie, best running strategy, bodybuilding tips) and were therefore excluded. We then selected for further analysis the 3 most popular (based on active user activity) subreddits from the categories identified: PA/sports-related (/r/bodyweightfitness, /r/crossfit, and /r/Fitness) and general advice (/r/askMen, /r/askWomen, and /r/askReddit).

To analyze the dataset generated by our data collection and filtration procedure, we used the following analysis pipeline that included 5 steps, as presented in Figure 1. Following the established steps during the development and evaluation of machine learning and artificial intelligence models, it is essential to ensure the accountability and reliability of these models [28,29]. All data analysis was carried out in Python version 3.9.13 (Python Software Foundation) using the fuzzy-c-mean 1.7.2 (FCM), pandas 1.5.3, NumPy 1.26.4, and umap-learn-0.5.6 packages.

The first step aimed to explore the data and create initial clusters of topics (ie, motivations for engaging in PA) that appear in the text. First, we created embeddings, or vector representations, of each sentence to use for the clustering. Specifically, we encoded using the Sentence Bidirectional Encoder Representations from Transformers language model. We then reduced the high dimensionality of the embeddings using Uniform Manifold Approximation and Projection for Dimension Reduction, introduced by McInnes et al [30], implemented in umap-learn. Finally, we used a clustering algorithm FCM, with FCM implementation [31] to find groups within the data with the most similarity in the same cluster and the most dissimilarity between different clusters (this approach was chosen due to the observation that our data contains examples that discuss several topics). Specifically, this step allows finding groups of similar comments (which are converted to numerical data points) in the data by calculating the distance between data points (see subheading A.1 in Multimedia Appendix 1).

To verify the clusters’ topics, one of the co-authors (IH), with expertise in exercise science and motivation, reviewed several samples and proposed a topic title for each cluster. Then, 30 samples were randomly selected from each cluster, and 2 additional reviewers with similar expertise independently decided whether each sample matched the proposed cluster topic. Note that a sample could match several topics simultaneously. If both reviewers agreed on the topics, the sample was added to a pool of corrected annotated samples. Thus, at the end of this step, we had a high-quality representation of the different topics in the dataset.

We classified the remaining nonreviewed comments into the topics identified in steps 1 and 2. This procedure’s goal was to automatically classify the textual comments into one or more defined topics (Multimedia Appendix 2 [32-35]).

We implemented an additional round of expert evaluation of the classified comments. To minimize the risk of researcher bias, we recruited 5 graduate-level exercise physiology students rather than using the original 3 experts. We randomly sampled 30 instances from our data (not used in the first round of evaluation) and asked each expert to evaluate the classified data. Each expert was presented with a dedicated user interface (Figure 2). Each sample was displayed along with the possible topics (multiple choices were allowed) and a field to add comments using free text. Before beginning the annotation task, the reviewers (5 graduate-level exercise physiology students) underwent a familiarization procedure by reading the instructions and practicing on 3 fictitious examples, similar to comments from the dataset regarding topics and average comment length. Once the annotation task was completed, we aggregated the results and evaluated the quality of the classifier and the interrater agreement.

To ensure reliable findings, the quality of the supervised classifier that identifies motivational factors and strategies was evaluated using both automatic metrics (precision, recall, and F₁-score) and an expert evaluation panel, as mentioned above in step 4. For the human evaluation, we used Fliess κ interrater reliability [36].

This research did not require approval from the Tel Aviv University Ethics Committee as it was based on publicly accessible information that is legally protected and does not necessitate an ethics review. This position was confirmed by the head of the university’s ethics committee. From a privacy perspective, we used data from Reddit, a platform where most discussions are publicly accessible to anyone, with or without a Reddit account (except for private subreddits). In addition, Reddit offers an extensive API that allows access to much of the platform’s information. Strictly adhering to the rules and ethical guidelines of each subreddit, we only collected data from subreddits that explicitly allow research activities. Furthermore, to protect user privacy, we only recorded the comment ID without including any user information or the content of the comment itself, ensuring no personally identifiable information or data that could lead to reidentification was collected.

We collected 2387 comments from 70 discussions published between 2017 to 2021. After completing the data filtration procedure, 1850 samples remained, submitted by 1850 unique users: 227 from /r/bodyweightfitness, 152 from /r/Fitness, 220 from /r/crossfit, 338 from /r/askReddit, 479 from /r/askMen, and 434 from /r/askWomen. Links and examples of the discussions can be found under subheading A.3 in Multimedia Appendix 1.

Our model classified the comments into 10 distinct topics, which we observed could be divided into 2 broad categories: motivational factors for initiating PA (motivations) and strategies or suggestions on staying motivated and adhering to PA (strategies), for a total of 1740 observations of identified topics, over 1247 comments. The motivations for participating in PA (frequency of appearance) were physical appearance (298/1247, 23.9%), physical health (235/1247, 18.9%), and mental health (211/1247, 16.9%). The main strategies people use to adhere to PA were habit formation (373/1247, 30%), goal setting (173/1247, 13.9%), choosing enjoyable activities (151/1247, 12.1%), socializing (121/1247, 9.7%), using media (111/1247, 8.9%), using different apps to monitor PA (35/1247, 2.8%), and financial commitment (32/1247, 2.5%). Representative examples per topic are presented in Table 1, with their sums and proportions in Table 2. For a breakdown of topic frequency by subreddit, see subheading A.4 and Figure S1 in Multimedia Appendix 1. The reliability and accuracy metrics of the topic allocation by the model were high. We observed substantial agreement between raters with a Fliess κ of 0.621. The subsequent automatic evaluation of the model’s precision, recall, and F₁-scores were 0.86 for all metrics. For a detailed breakdown of this process, see subheading A.4 in Multimedia Appendix 1.

Our objective in this study was to explore motivational and adherence strategies for engaging in PA by using an unsupervised, multistep clustering technique to gather and analyze comments from Reddit forums. Our model classified the comments into 10 distinct topics, which we further categorized into 2 themes: motivations for initiating PA and strategies for maintaining motivation and adherence to PA. The main motives for individuals to engage in PA was their physical appearance (298/1247, 23.9%) and enhancing physical (235/1247, 18.9%) and mental (211/1247, 16.9%) health. The most prevalent strategy individuals used to adhere to PA was habit formation (373/1247, 30%), followed by goal setting (173/1247, 13.9%), enjoyable activities (151/1247, 12.1%), socializing (121/1247, 9.7%), using media (111/1247, 8.9%), using PA monitoring apps (35/1247, 2.8%), and financial commitment (32/1247, 2.5%). In addition, the frequency with which certain topics were mentioned together presented with certain patterns (Figure 3). For example, mental or physical health were often mentioned together with physical appearance (385/1247, 22% and 348/1247, 20%, respectively), and app monitoring or media use were often mentioned together with physical appearance (348/1247, 20% and 348/1247, 20%, respectively). These results shed some light on the multifactorial motivations underlying PA and hint at certain strategies individuals may gravitate to, depending on their primary motivational factor to initiate PA.

Comparing and contrasting our results to other studies using more traditional approaches presents challenges due to the different methodologies implemented. Nevertheless, our results are partly aligned with studies assessing PA motivation through questionnaires based on the Self-Determination Theory (SDT) framework, such as the Exercise Motivation Measurement scale (EMI-2) [38]. The EMI-2 takes the SDT spectrum of motivations and further divides them into subscales based on various topics (eg, “ill health avoidance,” “social recognition”). For example, we identified 3 motivations for participating in PA (physical and mental health and physical appearance) consistent with the subscales identified in such studies. This supports our model’s construct validity and SDT-based questionnaires’ external validity, as our unprompted findings closely mirror several subscales of the established questionnaires. Yet, our results are also misaligned with some of the literature. Regarding the EMI-2 scale, our model failed to identify specific “personal” and “social” motivation-related subscales, such as challenge, enjoyment, social recognition, and affiliation. We speculate that this misalignment stems from the unique characteristics of Reddit (public, online, anonymous posts) and the characteristics of the population that tends to use it (younger, male, western internet users) who may feel less inclined to discuss more personal motivations openly. We make this assumption with the caveat that we do not know if the demographic attributes of the subreddits we sampled truly mirror those of Reddit at large.

Our results seem to better align methodologically with qualitative studies based on semistructured interviews, showing a similar distribution of motivations in health-related topics [12,39,40]. For example, Ashton et al [40] identified 4 themes for motivators for PA among young adult men (a similar demographic to the average Reddit user): physical and mental health, physical appearance, social inclusion, and sport or performance. Here, we see that, similar to the quantitative literature, our model aligned with the health-related categories but did not identify social and personal motivational themes, such as skill development, support networks, community, relationships, and competitiveness. Direct comparison to qualitative studies on the topic is also challenging, given that they generally use thematic analysis as their research tool of choice. In contrast, we quantified the prevalence of topics within a large sample, as presented in this study, in a manner similar to qualitative content analysis. Furthermore, the process we used for assigning names to topic clusters, performed by a single reviewer without a formalized content analysis process, might have led to the misclassification of specific categories. Yet, a careful review of the text categorized by our model reveals a clear distinction between discussions on “how” to motivate oneself to engage in or adhere to PA versus the underlying “why” behind engaging in PA. We acknowledge this as a consideration for future research using wide-scale, self-disclosed content and recommend implementing established methods from the field of qualitative content analysis [41].

Several limitations of this study are worthy of discussion. First, the lack of data on the demographic attributes of the comment authors beyond platform-wide industry reports [25] limits our ability to associate the observed motivations and strategies with specific populations (ie, gender and age groups). Furthermore, although there are certain natural language processing techniques that can attempt to infer a user’s gender based on previous comments written by the user, in our case, they were not able to assign gender to a large portion of our sample and thus excluded from the final analysis. Second, it is important to note that the questions that our analysis can answer are limited in scope as it was meant to be a proof-of-concept. The relatively simple queries we constructed were not designed to identify posts containing more detailed information, such as whether users were meeting minimal exercise standards, the location of exercise, or the results of implementing the identified strategies. In addition, all self-disclosed information, especially internet-based and pseudo-anonymous, must be interpreted with caution as the difference between disclosure and reality may be significant. Third, the “data-driven research” process used here allowed for substantial researcher degrees of freedom during the initial data selection and filtration process, potentially leading to selection bias, among others. Future studies could implement a more formalized process for topic classification and setting inclusion or exclusion criteria, enabling the answering of more complex questions and further refining our approach. Despite these limitations, incorporating human experts in auditing the automated results throughout the different stages of the analysis process is an essential strength of our methodology.

Our study introduces a novel usage of language models to harness the content shared on social media platforms to investigate the motivations and adherence strategies of individuals engaging in PA. Our findings further support the established literature and identified self-reported strategies implemented by this population for initiating and sustaining PA. The insights gained here provide valuable information and research tools for better understanding PA motives and adherence strategies. By leveraging self-disclosed data in an unprompted, nonresearch context on a large scale, we have expanded possibilities for research and gained more profound insights into people’s thoughts, feelings, and opinions. This method has been successfully applied to various fields, and its application to PA and motivation represents a significant contribution. This study provides proof-of-concept for future investigations and opens new avenues for using language models in social media channels to study PA behavior and motivation.