Dementia is a clinical syndrome that severely impairs a person’s memory, language, and reasoning abilities [1]. Alzheimer disease is the most common cause of dementia, and approximately 6.5 million Americans aged ≥65 years live with Alzheimer disease or related dementias (ADRD) [2]. Owing to a lack of effective treatments, 80% of persons living with dementia are cared for at home by their informal caregivers (eg, family members, friends, or other unpaid caregivers) [3]. This results in tremendous physical, financial, and emotional challenges for these caregivers [4,5], making them known as invisible second patients [6]. It has further been shown that informal ADRD caregivers in different kin relationships (eg, adult children, spouses, or children-in-law, which we refer to as relationships in this paper for simplicity) differ in their caregiving experience, physical and mental health, and support needs [7,8].

To date, research on how to support informal ADRD caregivers has focused primarily on the support provided by health professionals [9] (eg, cognitive-behavioral therapy [10], benefit-finding interventions [11], and educational program [12]). Although professional support can improve caregiving skills and emotional well-being, it is challenging to implement it on a large scale because of limitations in workforce availability and financial support [13-15]. At the same time, an increasing body of theoretical and empirical studies indicates that peer-based social support from offline social networks has a positive impact on informal ADRD caregivers [16,17]. For example, the source-need matching theory [18-21] suggests that social support, including informational and emotional support, from caregivers who share similar social backgrounds or caregiving experiences, is more beneficial for stress coping than the support from those who do not. Notably, research has shown that informal ADRD caregivers with shared caregiving experiences can help each other with unique informational and emotional support [22]. However, similar to professional services, offline peer support also faces scalability issues. For example, offline informal ADRD caregivers often report small, high-quality networks of only 4 people on average. By contrast, those with larger networks tend to report experiencing less role strain and better well-being [23].

The integration of the internet into daily life has enabled caregivers to increasingly discuss health-related topics on online platforms [20], including Twitter [24], Facebook [25], and other online communities [26]. In recent research, higher emotional stress and financial hardship of caregivers were found to be the most significant emerging factors associated with increased health-related internet use [27]. Participation in online peer discussions can reduce depressive symptoms [28], improve quality of life [29], and reduce feelings of loneliness [30]. Caregivers seek support and are willing to share experiences and practical information that they believe will assist other caregivers in online environments [31]. In addition, numerous online communities offer the added advantage of facilitating asynchronous discussions and connecting caregivers without the limitations of time and location. These advantages enable ADRD caregivers to receive sufficient social support while fulfilling the time and space obligations of providing care [32]. However, the studies to date that analyzed online caregiving discussions have paid limited attention to the nature of the relationships between informal ADRD caregivers and persons living with dementia. In this respect, it should be recognized that spouses and adult children constitute the 2 largest types of primary informal ADRD caregivers. Spouses regard caregiving as part of their marital obligations, whereas adult children consider such tasks as an important change in their lifestyle [33]. Moreover, spouses tend to be less selective about whether to take on caregiving responsibilities and are at a higher risk of developing health complications compared with adult-child caregivers [34]. By contrast, adult children tend to experience greater family conflict or work disruptions than spouses [33,35]. Given that there are likely differences in caregiving experiences, characterizing the online behaviors of different types of caregivers by relationships can help design customized solutions to online social support that can efficiently assist each type of informal ADRD caregiver.

Most online platforms (eg, Reddit) do not establish specific forums based on each type of caregiver relationship, such that all caregivers discuss topics within the same forum. Even in online communities with separate forums for different types of caregiver relationships (eg, Talking Points managed by the Alzheimer’s Society in the United Kingdom), not every caregiver discloses their relationship with persons living with dementia publicly, and any caregiver can publish their posts in any forum that they believe is appropriate. Considering the massive number of online sources that may contain ADRD caregiving discussions, it is crucial for researchers to develop machine learning algorithms that can efficiently identify the relationship between caregivers and persons living with dementia based on their online posts, which will be a valuable social determinant that can be applied to online social support-based research. Various machine learning algorithms have been developed to extract relevant information from massive, noisy online data to facilitate biomedical research [36], including, but not limited to, predicting mental health status [37], disseminating study information during the COVID-19 pandemic [38], and learning the needs of patients with ovarian cancer and their caregivers [39]. However, most studies in the ADRD caregiving field have either identified online ADRD caregiving discussions [26] or summarized caregiving challenges [35]. Few studies have differentiated caregivers’ relationships with persons living with dementia.

In this study, we aimed to conduct a comprehensive comparison of the online behaviors of the 2 distinct groups of informal ADRD caregivers: adult-child and spousal caregivers. To achieve this goal, we conducted topic analysis, sentiment analysis, and caregiver relationship classification using the large-scale retrospective data collected from ALZConnected [40], an open online community powered by the Alzheimer’s Association for any person affected by ADRD in North America. Our study not only enriches our understanding of the online behaviors of informal ADRD caregivers in online communities but also highlights the potential for improving online support systems, thereby contributing to the advancement of targeted support for informal ADRD caregivers.

This study was exempted from human participants research by the Institutional Review Board of Vanderbilt University Medical Center (221732). The online posts are publicly accessible through the ALZConnected online community. In this paper, we only present results on an aggregated level, and any quotes presented are carefully rephrased to maintain the privacy of the corresponding users [41].

ALZConnected is a free online community for caregivers and persons living with dementia to post questions, offer and receive support, and make public and private groups around dementia-related topics and issues. ALZConnected allows individuals to read online discussions without registering on the platform. However, to use all the functions in ALZConnected, including publishing a post, individuals must register to become a member on the platform. During registration, a user is asked to provide information about their relationship with persons living with dementia. Registered users may choose to make such information visible to other logged-in online users. ALZConnected has 10 forums, each organizing online discussions into a collection of topic threads. Each thread is initialized with the first post as the top post and can contain many other posts called comments. We collected data from ALZConnected using a web crawler that we developed based on the BeautifulSoup v4.11 python package. The data contain online user interactions that transpired between October 2011 and August 2022. We maintained all users who self-reported as ADRD caregivers and excluded administrators and moderators.

For this study, we extracted all the posts published in three main caregiver forums: (1) Caregivers Forum, (2) Spouse or Partner Caregiver Forum (referred to as Spouses Forum for simplicity), and (3) Caregivers Who Have Lost Someone Forum (referred to as Lost Forum). Although it is unlikely that a caregiver is active online every day, in this study, we define the active days of a user as the number of days between the dates when the user published the first and the most recent posts to improve the readability of the paper. In our analysis, we focused primarily on adult-child and spousal caregivers; all other relationships were categorized as other caregivers.

We characterized caregivers’ online activities along several dimensions: (1) posts and relationships, (2) views and comments, and (3) posts and active days. To do so, we first identified all the self-reported relationships and the corresponding post volumes to provide a big picture with respect to all the online caregivers. Second, ALZConnected displays the number of views and comments for each topic thread publicly, which enables us to compare these 2 statistics on a log scale separately for adult-child and spousal caregivers. Reading and posting are the 2 types of essential activities in online communities that can benefit caregivers in different ways. Third, we compared the distributions of the online posts and active days on a log scale separately for adult children and spouses to gain insights into online activities for both short-term and long-term online caregivers. To avoid the logarithm of 0, we incorporated a weak uniform prior in the form of a small pseudo count. Specifically, this was accomplished by adding 1 to each count.

We applied structural topic modeling (STM) v1.3.6 [42], an R package developed by researchers and contributors, to top posts to investigate the topic prevalence for adult children and spousal caregivers. In general, a caregiver initiates a topic thread by asking a question or sharing their experience in the top post, whereas other caregivers make comments in the response below. Therefore, focusing on the top posts instead of comments enables an examination of the challenges communicated by these caregivers. Furthermore, focusing on top posts can reduce the bias induced by super-active users who published substantially more posts than others. STM allows for the integration of post-level metadata (eg, authorship) into the topic modeling process. In contrast to standard topic modeling methods such as latent Dirichlet allocation, STM can provide better intuition into the dynamics of social representations through a comparative view [43-45].

In our model training, we incorporated caregiver relationships as a binary meta-variable. This variable indicates whether a top post was composed by an adult child or a spouse. More specifically, we extracted the top posts from all the topic threads, removed stop words and special symbols, and dropped the words that appeared <10 times in the data set. To determine the optimal number of topics in STM, we relied on the measures of exclusivity, which refers to the distinctiveness of the words with the highest frequencies in the topic, and semantic coherence [46], which quantifies how the words in a topic frequently co-occur together in general contexts [42]. We chose the optimal topic number from a predefined list of [5, 10, 15, 20, 25, 30] by finding the one that made a trade-off between the 2 metrics.

To support the interpretation of the modeling results, we summarized each topic with a name and grouped similar topics together. This summary was developed by the authors through a manual review and discussion of the top words ranked by their probabilities in each topic and the 5 posts with the highest probabilities of each topic. We also compared adult children and spousal caregivers with respect to topic prevalence. The effect for each topic was estimated by regressing the proportion of topics on a binary meta-variable that indicates whether the top post was composed by a spousal caregiver as opposed to an adult-child caregiver. Positive (or negative) effects indicated that the corresponding topics were more likely to be posted by spousal (or adult-child) caregivers.

Sentiment analysis refers to a common natural language processing technique that applies computational methods to determine whether a given post conveys positive, negative, or neutral emotions or tones. In this study, we define the relative days of a post as the number of days since the first post of its author was published. By realigning all the posts along their relative days rather than published dates (eg, October 19, 2022), we analyzed how the sentiment of a caregiver’s posts changes on average as the caregiver interacts with others in the community over time.

In recognition of the measurement bias that can be introduced by applying off-the-shelf models to this data set, we relied upon 2 existing popular sentiment tools to perform the analysis, which we believed could provide better intuition than the application of a single model. The first tool is the Valence Aware Dictionary for Sentiment Reasoning (VADER) version 3.3.2 [47], which is a rule-based model to evaluate the sentiment of a given text [48]. VADER generates a normalized, weighted compound score that ranges between −1, the most extreme negative sentiment, and +1, the most positive sentiment. The second tool is Linguistic Inquiry and Word Count (LIWC) 2015 [49], which calculates the percentage of each linguistic category by mapping the words in a given text into a predefined word list of the linguistic category [50]. This tool has been widely adopted in online content-based research [51]. In this study, we focused on the negative emotion category in LIWC.

The optimal number of topics was determined to be 25. Table 2 presents these topics, along with their most representative words (top words) based on the highest word probabilities in a topic and the expected topic proportion in the entire data that were used to train the STM.

Caregiving discussions were mainly focused on 4 primary topic groups: daily caregiving issues, disease related, sentiment and relationship, together with some common verbs in online communications, which we categorized as others. In particular, the disease related topic group had the largest proportion, contributing 32.3% of the discussion. The second largest topic group was about daily caregiving matters, which included #13 (sleeping issues; 3.9%; eg, “My mom didn’t sleep well last night, and now she’s napping during the day. Should I let her nap?”) and #11 (financial issues; 2.8%; eg, “I am now my mom’s social security payee and I had to use my [bank name] to intervene in my mom’s account because she gave the money to a scammer”) and other issues that were commonly reported in the literature [53,54]. The emotion topic group included #7 feelings (4.4%), #2 gratitude (4.3%), and #3 blessings (3.8%), which mainly describe the mixed emotions they experienced. The relationship topic group was embodied by topics #6 (2%) and #8 (2.8%), both of which were about relatives. On the basis of the top words and their related posts, topic #6 was mentioned more in family conflicts, whereas topic #8 was mentioned more in other scenarios.

Figure 3 compares adult children and spousal caregivers with respect to topic prevalence. There are several findings to highlight. First, although there was a smaller number of spousal caregivers than adult-child caregivers in ALZConnected, spouses discussed a wider range of topics than adult children, including #14 (traveling), #1 (transportation), #25 (dementia), #17 (COVID-19), #18 (showering), #15 (loss of memory), and #16 (disease diagnosis). This aligned well with the literature, which indicated that spousal caregivers report a need to redefine their role and relationship with persons living with dementia [55].

Second, the 2 topics related to #6 and #8 (relatives) and #24 (nursing home) were more frequently discussed by adult children (eg, “my mother will be moving to my sister’s house and she will become my mother’s full time caregiver.... My sister refuses to learn about dementia caregiving. I suggested...move to her house with my mother for a few weeks...she thinks this is unnecessary.”). This suggested that adult children faced more family conflicts when caring for persons living with dementia than spouses [33]. In addition, adult children seemed to discuss the medical topic (#21) more frequently than spouses.

Third, in the emotion topic group, adult children posted content about “praying” in the forums, such as topic #3 blessings (eg, “And I will ask the Father...give you another Helper...to be with you forever”), whereas spouses expressed more personal feeling during daily care, either gratitude or sadness, such as topic #2 gratitude (eg, “I remember when: We laughed together We cried together.... We loved life together We got through tough times together”) and #7 feelings (eg, “I feel guilty...for not being patient with my DH...guilty about mentioning how hard it is to people who really don’t understand and think I’m exaggerating”).

Figures 4 and 5 illustrate how sentiment changed over time by the VADER compound score and LIWC negative emotion, respectively. To improve readability, we averaged the VADER compound scores or LIWC negative emotion for each of the caregivers during an active day into a single point. The linear interpolation and its 95% CI are shown in Figures 4 and 5.

As shown in Figure 4, which corresponds to the VADER compound score, the average sentiments for both caregiver types were consistently >0.4 and maintained an increasing trend. Compared with spouses, adult children exhibited a higher growth rate in the VADER compound score. The Spearman rank-order correlation between the VADER compound score and relative days for adult children and spouses were 0.42 and 0.17 (P<.001), respectively. This observation is in agreement with a previous finding that spousal caregivers expressed both gratitude and negative feelings relatively more than adult-child caregivers [56]. Meanwhile, according to the LIWC negative emotion, as shown in Figure 5, both caregiver types exhibited a decrease in negative emotion over time. The Spearman rank-order correlation between the LIWC negative emotion and relative days for adult children and spousal caregivers were −0.36 and −0.44 (P<.001), respectively. The improved sentiment over time echoes the finding of a prior study that showed that a caregiver’s engagement in an online community is related to a reduction in depressive symptoms [21].

This study reports on the online behaviors of adult-child and spousal caregivers in a large online community. There are several principal findings of merit. First, ADRD caregivers are diverse in terms of their relationships with persons living with dementia as well as in the ways they use social support in the online community. For example, the high volume of views in ALZConnected indicates that many online users are “lurkers” or collective knowledge consumers who mostly read online discussions [59]. However, these online caregivers are often neglected by researchers because of a lack of their online voices. Moreover, a majority of the online caregivers who write posts, known as collective knowledge producers, are short-term users. This brevity in platform use is most likely an artifact of their focus on addressing a specific caregiving issue. In recognition of this observation, in addition to the typical goal in the literature to improve caregivers’ long-term well-being [28,60], research should aim to solve the short-term needs of most online caregivers as well.

Notably, there were more adult children than spousal caregivers in ALZConnected. This may be partly because the younger generation is more likely to use the internet. However, spouses tended to publish relatively more posts and actively participate longer in ALZConnected than adult children. Spousal topic threads also received more comments than adult children’s topic threads. Adult-child and spousal caregivers shared many common concerns about issues related to living, sleeping, diet, and finance. Spouses were more likely to discuss other caregiving-related issues such as showering, transportation, disease diagnosis, and the COVID-19 pandemic. This may be due to the close physical bond that a spouse has with the care recipient, the emotional bond, or either a combination of both, as suggested in previous research [7].

Our sentiment analysis showed that both caregiver types experienced an increasing positive sentiment over time in this community, indicating a positive impact on the emotional well-being of caregivers who pursued long-term online support. This is consistent with findings from observational studies that indicate that participating in online communities reduces caregivers’ stress levels [28,30]. However, spousal caregivers had relatively lower sentiment improvement, which, together with their wider range of caregiving discussions (Figures 4 and 5), suggests that they experience more complex challenges than adult-child caregivers.

The best classifier obtained an AUPRC of 81.3% on the user-level prediction. From an algorithmic perspective, our work contributed to an important biomedical research field in estimating online users’ sociodemographic factors from their posts [61,62] by accurately predicting online caregivers’ relationships with persons living with dementia based on their posts. relationships with persons living with dementia based on their posts. Specifically, these factors can be effectively adjusted in any statistical analysis related to online ADRD caregiving discussion. In addition, given that informal dementia caregivers often share their experiences on any online platform they prefer, our classifiers can be applied to help caregivers locate the posts that are published by the same type of caregivers, which will be likely to meet their specific caregiving needs.

The observed decreasing trend in the probability curves on the posting timeline for both caregiver types suggests that online caregivers tended to change or expand their discussion scope over time. This is because these long-term online caregivers are more likely to support other caregivers by composing comments. Community organizers should consider fostering these “veteran users” to sustain the community. Again, the trend of a relatively lower decreasing prediction probability for spousal caregivers provided more evidence that they behave differently from adult-child caregivers. Therefore, tailored strategies should be devised to assist these caregivers based on their relationship type. For instance, spousal caregivers could benefit from an emphasis on daily caregiving matters and emotional support, whereas adult-child caregivers might find greater value in discussions concerning nursing homes, medications, symptoms, and family conflicts. The error analysis showed that the model was more likely to make incorrect predictions between adult children and other caregivers, who, based on a manual examination of the posts, were found to be more likely to be the children-in-law of persons living with dementia. This suggests that children-in-law caregivers, although a small proportion, may share similar caregiving experiences or challenges with adult-child caregivers and should receive attention from the research community and society as well [63].

More broadly, our findings imply that open online communities such as ALZConnected can be leveraged to provide sufficient informational or emotional support to a wide range of informal ADRD caregivers, especially those who live in rural areas where local support services or resources are generally limited [64] and those who hesitate to seek offline support owing to stigma [65]. However, any such translational research is recommended to consider the following issues to ensure fairness and effectiveness of accessing and using online social support: (1) the digital divide caused by either unavailable high-speed internet, low eHealth literacy, or intensive caregiving activities [66]; (2) the misinformation or misconceptions regarding diseases or related treatments [67]; and (3) any potential hatred language that is not uncommon in online environments [68].

Despite the merit of our findings, there are several limitations of our study that can serve as the basis for future research.

First, our research focused on a single online community, which may have limited the generalizability of our findings. Therefore, it is necessary to validate our findings using other online communities. However, as ALZConnected is the largest ADRD online community in North America, these findings should be informative to guide future research in this area. Second, we were unable to examine the online behaviors of “lurker” caregivers or include the sociodemographic characteristics (eg, age, gender, sexuality, race, and ethnicity) of online users in our analysis because of the lack of availability of such data in general online environments. Thus, we believe that future research can consider incorporating surveys with users to complement the research findings drawn from merely online posts. Third, we only examined the sentiment encoded in the online discussions. There is clearly an opportunity for investigations that integrate observational research into the study to help measure the psychological well-being of online caregivers, which, together with computational methods, examines how exactly online interactions could improve a caregiver’s well-being. Fourth, we did not evaluate the prediction models in other online communities. This is deemed to be outside the scope of this study, as it requires an intensive survey to collect gold standard relationship information. However, given the promising outcomes of this research, we believe that the built models can be easily adapted to predict the kin relationship between caregivers and care recipients based on the posts in other online ADRD caregiving communities, which can be achieved by using either transfer learning [69] or retraining the models using new annotated data.

To the best of our knowledge, this is the first study to examine and compare the online behaviors of adult children and spousal ADRD caregivers in a large open online community. Our results show that these 2 caregiver groups exhibited different online behaviors, with spousal caregivers posting more frequently and discussing a wider range of caregiving issues. These observations indicate that spouses experience more complex challenges than adult children. Researchers and community organizers should take into account the heterogeneity of online behaviors, which is likely to be owing to different caregiving experiences, to improve the online experience of different types of caregivers. Further research can be applied to explore how online interactions can address specific caregiving challenges at the topic thread level and benefit both short-term problem-solving and long-term social integration.