Procrastination, an irrational and malicious behavioral pattern defined as the intentional delay of important or necessary tasks despite knowing the negative consequences [1], affects nearly one-fifth of adults worldwide [2,3]. Despite the phenomenon often being described with a bit of humor, as evidenced by a popular Technology, Entertainment, Design Talk [4], it poses a serious threat to mental and physical health. Studies have demonstrated that procrastination can cause stress, depression, and anxiety [5-7], as well as raise the risk of cardiovascular diseases and hypertension [8]. Overall, those affected by procrastination experience a concerning risk of a decline in life quality [9].

Correspondingly, medical research has engaged in numerous treatment directions to help and support those affected [10,11]. Acknowledging that procrastination primarily results from a failure of self-regulation [2], which is influenced by both situational (eg, task characteristics, expected outcome, and delay) and motivational (eg, self-efficacy, self-belief, and impulsiveness) determinants [12], cognitive and behavioral interventions as well as their combination in cognitive behavioral therapy promise to be most successful [10,13-15]. However, traditional treatment of procrastination still suffers from low attendance and adherence rates compared to the amount of people affected [16]. In this regard, digitally supported treatment of procrastination using behavior change support systems (BCSSs) has gained attention as a low-threshold, accessible alternative to in-person therapeutic approaches [11,14,17].

BCSSs are systems “with psychological and behavioral outcomes designed to form, alter or reinforce attitudes, behaviors or an act of complying without using coercion or deception” [18]. They have been demonstrated to help restructure malicious behaviors that pose health threats [19], such as obesity [20-22], lack of physical activity [23,24], unhealthy nutrition [25-27], or smoking [28-30]. Through various behavior change techniques (BCTs) that target situational and motivational behavior determinants, such as goal setting and self-monitoring, context restructuring, reframing of beliefs, or the illustration of behavioral consequences [31], BCSSs can support a stepwise transition from health-threatening behaviors to novel behavior patterns and habits [32]. A recent review of primary studies in the field of BCSSs for procrastination [17] demonstrates that such BCSSs can evidently help raise awareness of distractions [33-35], reduce procrastination behaviors [34,36,37], and increase self-efficacy [37-39].

While these studies are still often tied to augmenting traditional cognitive behavioral therapy with digital applications under medical guidance, it is only in the last few years that BCSSs with motivational design approaches for the successful self-treatment of procrastination have gained traction [17]. This development can be attributed to the emerging research trend of motivational information systems (ISs) [40,41], which combine functional, utilitarian components (such as support in planning and goal setting) with hedonic (referring to a sense of pleasure [42]) and eudaimonic (referring to a sense of meaningfulness [42]) design elements. The latter aim to evoke positive experiences in using the system and performing the target behavior, such as joy, excitement, or curiosity, and satisfy motivational needs [40,41]. Initial studies show that motivational BCSSs for procrastination that combine educational content about procrastination stimuli with quiz and achievement mechanics can significantly reduce procrastination in unguided self-treatment [43]. Moreover, gameful storytelling in task planning and work on tasks can enhance feelings of self-control [44].

Research on motivational BCSSs for low-threshold and accessible self-treatment of procrastination is still nascent, but practical efforts have produced a plethora of (primarily mobile) apps that use motivational design approaches to support people in handling procrastination behaviors [44]. These include gamified task management platforms that integrate narrative design [45]; artificial companions that provide approval and help with planning and self-reflection [46]; and digital tools that use focus timers, unlockable collectibles, and team focus sessions [47], with several apps exceeding a million downloads. In this regard, there is great potential for research on motivational BCSSs for procrastination to build on and learn from these practical efforts [17] and investigate how different motivational design approaches affect the procrastination process as a basis to develop targeted recommendations for individuals seeking support in dealing with procrastination.

In this context, previous research has demonstrated that effectively designing health BCSSs requires not only experimenting with different BCTs that, in untargeted combinations, can cause counteracting mechanisms and result in highly ineffective solutions [48]. Rather, to provide an informed foundation for both researchers and designers on how to successfully approach the problem of procrastination with motivational BCSSs, it is crucial to identify and characterize commensurate combinations of BCTs and take account of their underlying theoretical mechanisms [48]. To this end, identifying the main archetypes of design that have emerged to target situational and motivational determinants of procrastination in practical applications is particularly valuable. Design archetypes represent a practice-oriented perspective on design knowledge that abstracts from single applications, which might sometimes combine BCTs randomly, to identify industry best practices. Thus, design archetypes can be regarded as complementary to conceptual, theory-driven design knowledge (eg, design principles and frameworks) in guiding developers when designing and implementing motivational BCSSs by offering prescriptions for effective design [49]. For researchers, knowledge about design archetypes further serves as a feedback mechanism that shows whether and, if so, how research insights have been transferred to real-world systems and where gaps between research and practice remain [50].

This study aimed to provide this foundation for both future research in investigating motivational BCSSs to combat procrastination and further practical efforts to design and select motivational BCSSs for self-treating procrastination by answering the following research question: what are prevalent archetypes of motivational design in behavior change support apps for procrastination?

To answer this research question, in line with previous studies that have systematized design in (mobile) health apps [50-52], we adopted a systematic screening and coding approach that built on the recognized Behavior Change Technique Taxonomy (BCTT) by Michie et al [53] and the intrinsic motivations for system use classification by Lowry et al [54] to form the basis for a 2-step cluster analysis according to Punj and Stewart [55]. Thereby, we identified clusters of motivational design approaches in prevailing motivational BCSSs for procrastination and then discussed the archetypes of motivational design characteristics for each identified cluster.

Consequently, our study systematically classified motivational design approaches in BCSSs to combat procrastination, providing 3 main contributions to research and practice. First, it constitutes the basis for future research efforts to investigate the comparative effects of motivational design archetypes on various situational and motivational determinants of procrastination, possibly paving the way for more targeted and individualized self-treatment recommendations through motivational BCSSs depending on the individual problem profile [1,56,57]. Second, it represents blueprints of design opportunities for developers of BCSSs to support the self-treatment of procrastination using motivational design approaches that increase system use and counteract dropout rates, a problem known from health-related digital applications [58]. Finally, it systemizes the plethora of available apps for both health professionals and individuals seeking support in handling procrastination as a basis to decide on a design archetype that best suits the patient’s or individual’s needs and preferences, thereby potentially increasing treatment adherence and counteracting the severe mental and physical health consequences of ongoing procrastination.

To answer our research question, we followed a systematic screening and coding approach of existing mobile apps, complemented by a 2-step cluster analysis [55] informed by previous work that has conducted archetype analyses of health-related apps and platforms [50-52]. Accordingly, our research method consisted of 3 main steps: database setup and screening, app coding, and cluster analysis (Figure 1 [50-55]).

To set up the database of pertinent behavior change support apps to combat procrastination, we followed a systematic search strategy in the German and US Apple App Store and Google Play Store. The reason why we opted for these 2 platforms is that they represent the 2 largest app stores for iOS- and Android-based mobile phones, with the Google Play Store recording >110 billion app downloads and the Apple App Store reporting approximately 35 billion app downloads in 2023 [59]. Moreover, the United States is the most significant Western mobile app market in terms of downloads and consumer spending on mobile devices [60], and Germany represents the largest mobile app market in the European Union [61]. We searched for apps related to the terms “procrastination” or “Prokrastination” (the German equivalent) in March 2024, leading to an initial set of 1259 apps (Figure 2).

After removing duplicates, of the 1259 apps, 612 (48.61%) unique apps remained for the initial screening. Thus, we screened the descriptions of these 612 apps in the Apple App Store and Google Play Store against our predefined inclusion and exclusion criteria, listed in Textbox 1.

Specifically, concerning their topicality, we excluded apps flagged as not maintained by the app stores. We also excluded paid apps that were not accessible without upfront payment as we wanted to focus on apps that are readily accessible to a broad audience of people affected by procrastination without posing a payment hurdle that might inhibit their benefit compared to traditional treatment approaches. Moreover, we excluded apps that were in languages other than English or German (eg, Chinese or Russian). In terms of their content and focus, we explicitly searched for apps that targeted procrastination of individuals at work or in daily life by providing specific utilitarian functionalities or content to approach procrastination (ie, the postponement of important or necessary tasks) on a cognitive (eg, specific learning or reflection content or guidance related to procrastination) or behavioral (eg, features to engage in goal and task planning or organization or focused work) level. Conversely, we excluded apps that were not focused on this problem, such as microlearning apps and general meditation apps (with content entirely unrelated to procrastination); household organizers; time-tracking apps for client projects or team planners (focused on organizing multiple people instead of supporting individuals); mood trackers and general habit trackers (without any content or functionalities related to specific tasks); or treatment apps for other mental health problems such as attention-deficit/hyperactivity disorder, depression, or anxiety (that were not suitable for individuals without pathological mental health issues). Moreover, as we focused on motivational BCSSs that are characterized by functional, utilitarian, and hedonic or eudaimonic design aspects [40,41], we excluded apps without any visible hedonic or eudaimonic design approaches, such as purely functional to-do lists, calendars, timers, or app blockers.

As a result of the initial screening process, of the 612 apps, 311 (50.8%) remained for the subsequent detailed analysis. In this step, we downloaded all the apps on an iPhone 15 Pro (for iOS apps) and a Google Pixel 4a (for Android apps) and analyzed their functionalities in detail against the predefined inclusion and exclusion criteria. During this step, we excluded an additional 59.2% (184/311) of the apps because they did not meet the inclusion criteria. Specifically, of the 184 apps, 34 (18.5%) were excluded because they had a paywall for use after installation (n=32, 94%) or could be accessed only via invitation (n=2, 6%); 16 (8.7%) were excluded because they were not usable (ie, they had been either removed from the app stores between screening in March 2024 and analysis in May 2024, n=6, 38%, or were buggy so that the main functionalities could not be used, n=10, 62%); 3 (1.6%) were excluded because they were only available in Turkish, French, or Chinese; 70 (38%) were excluded because they were not focused on the problem of procrastination; and 51 (27.7%) were excluded because they did not exhibit any hedonic design approaches. To decide whether an app entailed hedonic or eudaimonic design approaches, we juxtaposed them with our codebook based on the classification of intrinsic motivations for system use by Lowry et al [54] (Table 1) and excluded those apps that did not provide hedonic or eudaimonic features to evoke any of these defined motivations.

To ensure the reliability of our exclusion process, 3 authors took part in the detailed screening. One of them was a postdoctoral researcher in motivational ISs with 5 years of research experience in the field of BCSSs and motivational design, who set up the inclusion and exclusion criteria, defined the codebook, and led the screening and selection process. The 2 others were junior researchers with backgrounds in organizational psychology and business pedagogy. To ensure reliability, an intercoder agreement check was carried out after 40.2% (125/311) of the apps were analyzed. A total of 20% (25/125) of the analyzed sample was randomly selected and additionally analyzed by one of the other authors (specifically, the apps analyzed by the junior researchers were subjected to intercoder checking by the postdoctoral researcher, and the apps analyzed by the postdoctoral researcher were equally divided and checked by the 2 junior researchers). The intercoder agreement rate for inclusion and exclusion was 96% (κ=0.92), indicating nearly perfect agreement [62]. Any conflicts were resolved in a joint discussion, and the 3 authors progressed with the remaining apps. As a result, 127 apps remained as the final set of behavior change support apps for procrastination for the following research step. The complete list of included and excluded apps can be found in Multimedia Appendix 1.

For our app coding as a basis to identify motivational design archetypes, we opted for 2 existing classifications to code the utilitarian, functional design aspects and the hedonic or eudaimonic design aspects of the apps, which are both crucial for motivational ISs [40,41].

Regarding the functional design, we used the BCTT by Michie et al [53]. Michie et al [53] developed a taxonomy of 93 different BCTs organized in 16 hierarchical clusters: goals and planning, feedback and monitoring, social support, shaping knowledge, natural consequences, comparison of behavior, associations, repetition and substitution, comparison of outcomes, rewards and threats, regulation, antecedents, identity, scheduled consequences, self-belief, and covert learning [63]. The BCTT has proven its reliability and validity [31] and has already been widely used in studies to classify health applications and digital health interventions [19,64-68]. While alternative taxonomies for classifying design in BCSSs exist, such as the persuasive system design (PSD) model [69], which has also been extensively applied in research on health BCSSs [70,71], we decided to build on the BCTT for 2 reasons. First, unlike the PSD model, it incorporates cognitive techniques such as identity and self-belief. Given that previous IS research in the field of motivational BCSSs for self-treating procrastination has largely disregarded the use of cognitive techniques [17], it is all the more important to analyze whether such approaches have been incorporated into the practical design of motivational BCSSs. Second, PSD incorporates principles that are on the cusp of being considered utilitarian, such as liking (which refers to the aesthetic experience and, thus, is related to the hedonic experience of enjoyment [41]) or competition and praise (which can be considered hedonic elements aiming to evoke experiences of challenge and approval [54]). However, it does not encompass the full range of hedonic and eudaimonic aspects that could be included in apps, such as immersion, curiosity, or discovery [54]. Accordingly, we chose to use the BCTT as a taxonomy to classify the functional design of the analyzed apps complemented by a second classification explicitly targeting the hedonic and eudaimonic aspects of the apps. Consequently, for our analysis, we coded the apps based on the 16 overarching clusters of the BCTT; for example, an app was coded as meeting the BCT cluster self-belief if it provided functionalities for (1) verbal persuasion about capability, (2) mental rehearsal of successful performance, (3) focus on past success, or (4) self-talk, whereby the detailed definitions and examples for each BCT provided by the BCTT [63] served as our codebook.

In terms of the hedonic and eudaimonic design aspects of the apps [41], we opted for the classification of intrinsic motivations for system use provided by Lowry et al [54]. In their study, Lowry et al [54] list 11 intrinsic motivations for hedonic IS use based on a review of the IS literature: fun and enjoyment, escaping pressures, challenge, immersion, curiosity, discovery, self-expression, obtaining knowledge, experiencing autonomy/freedom, peer companionship, and approval [54]. According to their hedonic-motivation system adoption model, which has been extensively used to evaluate motivational BCSSs in the health domain [72-75], these hedonic motivations are pivotal to the adoption and continued use of systems that target hedonic experiences [54]. We defined each of these intrinsic motivations based on the Cambridge Dictionary and added meaning (ie, a sense of experiencing importance or value in an action [76]) as the 12th dimension to reflect the eudaimonic aspect of motivational ISs [40]. During the coding process, an app was coded to meet a specific dimension if it provided functionalities to evoke the described hedonic or eudaimonic experience (eg, of fun or challenge; Table 1).

In total, 3 authors performed the coding independently. Each app was downloaded, tested, and used to experience all features and functionalities. In this regard, we used each app intensively upon installation (ie, we clicked on all menu items, buttons, and learning content and used all available functionalities on the app to experience whether completing tasks or using features on the app resulted in system feedback or progress), and if the app included progression mechanics to unlock new features or notification and reminder mechanics, we also used the apps for several days to code all their features that might become visible over time. To ensure reliability in the coding process, we performed an intercoder agreement check after coding 30% of the apps, again randomly selecting 25 apps for intercoder checks and dividing the intercoder checks in a similar way to that for the screening check (ie, the apps coded by the junior researchers were subjected to intercoder checking by the postdoctoral researcher, and the apps coded by the postdoctoral researcher were equally divided and checked by the 2 junior researchers). The intercoder agreement rate ranged between 50% and 100% for the BCTs (with a mean agreement rate of 84%, SD 14%) and between 67% and 92% for the hedonic and eudaimonic design aspects (with a mean agreement rate of 82%, SD 6%), indicating good agreement [62]. As a result of the joint discussion, we streamlined our understanding of the BCTs (eg, defining that the social support technique only refers to support from other human beings and not from artificial agents on the app) and enlarged our codebook of hedonic and eudaimonic design aspects based on exemplary design features (Table 1) before progressing with the coding process. The final list of the 127 coded apps can be found in Multimedia Appendix 1.

We used cluster analysis to derive meaningful archetypes of app designs in the third step of our methodology. To do so, we followed the 2-step clustering approach proposed by Punj and Stewart [55] for 2 main reasons. First, this approach combines hierarchical clustering with iterative partitioning algorithms, thereby attempting to overcome the respective weaknesses of both approaches (eg, many partitioning algorithms, such as k-means, perform better than hierarchical algorithms but require an a priori definition of the number of clusters). Second, this methodology has been successfully applied to similar clustering tasks and has been shown to be useful for identifying design archetypes in various contexts [50,51,77].

In line with the approach by Punj and Stewart [55], our clustering analysis included 2 steps. In the first step, we used the method by Ward as a hierarchical clustering method to determine preliminary solutions and identify potential candidate numbers of clusters. Our coding included only binary data (ie, a design aspect, meaning each of the 16 BCT clusters of the BCTT [53] and each of the 12 hedonic or eudaimonic design aspects based on the classification by Lowry et al [54], is either present or absent). Thus, we chose the Euclidean squared distance as the similarity measure [50]. The dendrogram resulting from applying the method by Ward to our dataset indicated that 2 to 5, 7, 9, 11, and 12 were suitable candidate numbers of clusters. By examining the scree plot using the elbow rule [78], we narrowed down our selection to the 2-, 4-, 5-, 7-, 9-, or 12-cluster solution. On the basis of these candidate numbers, we conducted k-means clustering as an iterative partitioning algorithm. The 7-, 9-, and 12-cluster solutions produced by k-means all contained clusters with only 4 or even fewer apps, making reasonable interpretation of these solutions difficult. To decide on one of the remaining cluster solutions, 2 researchers manually compared their explanatory power by investigating differences and similarities in the BCTs and hedonic and eudaimonic design aspects included in our coding. First, we decided against the 2-cluster solution because the clusters were too heterogeneous in design aspects, making it impossible for us to derive meaningful interpretations. This left us with the 4- and 5-cluster solutions as candidates. After carefully analyzing both cluster solutions, we concluded that they were similar in many aspects but that one of the clusters in the 4-cluster solution was further divided into 2 distinct and informative clusters within the 5-cluster solution. Thus, we decided that the 5-cluster solution had the highest explanatory power and was the most suitable for deriving meaningful archetypes within this study. The cluster analysis results and the cluster solutions are included in Multimedia Appendix 2.

The final apps (N=127) that we included were fairly homogeneous regarding their categories, with most (n=122, 96.1%) categorized as productivity apps. In addition, 1.6% (n=2) were listed as lifestyle apps, 1.6% (n=2) were labeled as health and fitness apps, and 0.8% (n=1) were subsumed under the category of education, suggesting that, from a developer’s perspective, apps that support behavior change to combat procrastination are seen as primarily organizational tools rather than mental health support apps. Overall, the analyzed apps were well rated (mean 4.39, SD 0.48 stars), with 1.6% (n=2) of the apps with a rating <3 stars, 11.8% (n=15) with a rating between 3 and 4 stars, 23.6% (n=30) with a rating between 4 and 4.4 stars, 36.2% (n=46) with a rating between 4.5 and 4.9 stars, and 7.9% (n=10) with a rating of a full 5 stars. A total of 18.9% (n=24) of the apps that we included had not been rated at the time of analysis.

The principal findings of our study have significant implications for further research and practice. From a research perspective, our findings identified 5 main archetypes of motivational design in BCSSs for procrastination. These archetypes constitute a novel perspective on design knowledge for motivational BCSSs that abstracts from single, potentially ineffective real-world systems [48] and describes industry best practices that have prevailed in the design of motivational BCSSs for procrastination. For researchers, these archetypes provide a unique opportunity to obtain a holistic picture of how BCTs and hedonic and eudaimonic design elements have predominantly been instantiated in apps for procrastination. Researchers may juxtapose these findings with their own research endeavors, thereby potentially identify mismatches between current research foci and industry best practices. Consequently, they may discover new research opportunities or necessities to reallocate resources toward investigating the archetypal design configurations as possibly promising combinations of BCTs and motivational design that extend beyond the predominant focus on behavioral approaches [17].

Moreover, our observations indicate that the different motivational design archetypes may be suitable for targeting different determinants of procrastination [12]. Specifically, we found that apps in the self-improvement guide or emotional wellness coach archetypes primarily target outcome expectancies, whereas the structured progress monitor and productivity adventure seek to enhance outcome value and the social focus companion focuses on reducing impulsiveness [12]. While the archetypes were identified based on apps in the US and German markets and, therefore, predominantly reflect Western design approaches to target procrastination, research indicates that the primary determinants of procrastination, such as outcome expectancies, task aversiveness, and impulsiveness or self-control, are applicable and relevant across cultural contexts that are different from the Western one, including those of countries such as China, India, Israel, and Turkey [120-123]. Consequently, our design archetypes constitute a valuable foundation for further research, not only within the Western cultural context but, more broadly, to explore the potential of BCSSs as a low-threshold self-treatment approach for the global phenomenon of procrastination [2,3] across cultures. For example, given that individuals differ in their procrastination profiles and which of the determinants pose the most significant problem for them [1,56,57], there is great potential to examine how these motivational BCSS archetypes can serve as the basis for targeted self-treatment strategies for different individuals. The existing research has already called for implementing more individualized treatment strategies for procrastination [1,44]. Concurrently, the extant motivational BCSS research has demonstrated that, in addition to the treatment approach, individuals also express preferences for various hedonic and eudaimonic design elements that have the potential to influence their experiential impact [124-126]. Our findings provide a foundation for further research into how the different motivational design archetypes, in terms of both their treatment focus (behavioral, cognitive, or a combination) and their hedonic and eudaimonic design approaches, affect individuals with varying procrastination problem profiles, dispositions, situational contexts, and motivational preferences. In this way, targeted recommendations can be developed for individuals affected by procrastination.

Furthermore, our 5 motivational design archetypes present a blueprint for the effective practical development of BCSSs for procrastination. By demonstrating how designers can use and combine hedonic and eudaimonic design elements to enhance the efficacy of behavioral and cognitive BCTs in digital applications [40,41] while concurrently addressing dropout rates [58] and how designers can build on our archetypes as established configurations of design elements [49] rather than experimenting with inefficient combinations of BCTs [48], our study paves the way for an even more diverse array of motivational BCSSs to combat procrastination. Potential avenues for exploration include the integration of AI-based conversational agent approaches to enhance cognitive treatment strategies [95-98] and the use of virtual reality technology to unify cognitive and behavioral treatment and playful and narrative designs [127] with the objective of enhancing both the functional and experiential aspects of motivational BCSSs for procrastination. From the perspective of health professionals and affected individuals, our findings provide a basis for selecting a motivational design archetype that best suits the patient’s or individual’s challenges, needs, and preferences. Once this has been done, a suitable, existing app can be chosen from the results of our systematic cluster analysis. Consequently, our study provides a foundation for potentially enhancing the treatment adherence of individuals seeking mobile apps to assist them in addressing procrastination (as opposed to, eg, experiencing frustration due to the absence of effects from attempting to use an emotional wellness coach when the primary issue lies in stimulus control and distractibility). Therefore, ultimately, our findings assist in counteracting the significant mental and physical health consequences associated with procrastination [5-9].

It is essential to consider some critical limitations in interpreting the findings of this study, which have their roots in the methodological choices made.

First, we restricted our examination of motivational BCSSs against procrastination to smartphone-based apps. Despite this decision, our search and screening process demonstrates that we identified a multitude of motivational behavior change apps for procrastination. However, we recognize that there are other motivational BCSSs for the self-treatment of procrastination that rely on alternative technology, such as web applications [128], which were excluded from our cluster analysis. Furthermore, while the Apple App Store and the Google Play Store represent the 2 largest app stores for iOS- and Android-based mobile phones, there are other app stores for Android-based mobile phones, such as the Microsoft Store, that were not included in our search. Similarly, it is crucial to acknowledge that user settings, filters, and preferences in app store searches may impact the results obtained by entering identical search terms on disparate devices. Consequently, some users may not be able to access all the apps that we screened for use on their smartphones. In addition, it should be noted that our search and screening were limited to German or English search terms and apps because the United States is the most significant Western mobile app market in terms of app downloads and consumer spending on mobile devices [60] and Germany represents the largest mobile app market in the European Union [61]. As a result, it is possible that we overlooked valuable apps that were designed for the Chinese market and only accessible and functional with Chinese-language proficiency.

Furthermore, while our attention was directed toward motivational BCSSs due to their hedonic and eudaimonic design characteristics that can augment positive experiences [40,41] and, in turn, potentially increase self-treatment adherence, it is important to recognize that, within the realm of research, numerous BCSSs aimed at combating procrastination have demonstrated efficacy despite not relying on hedonic or eudaimonic design [17]. In this regard, our archetype analysis provides a foundation upon which the effects of different motivational design approaches can be investigated; however, it does not provide insights regarding their effectiveness itself. It is recommended that future research be conducted to build on this open avenue and explore the distinct effects of different motivational design archetypes compared to merely functional BCSSs for procrastination.

Ultimately, in recognizing that most studies in the domain of BCSSs for procrastination that rely on cognitive behavioral treatment strategies use BCSSs to augment existing therapeutic guidance [17], we restricted our cluster analysis to exclusively self-treatment motivational BCSSs, omitting the inclusion of therapeutic guidance. Although our objective was to concentrate on apps that have the potential to be low-threshold alternatives to therapeutic treatment [11,14], it is important to acknowledge that professional assistance may be essential and irreplaceable for individuals who are already experiencing serious mental and physical health consequences of procrastination. In such cases, it could even be irresponsible to provide them with a stand-alone solution that is not designed as a medical product. Consequently, an avenue for future research in the treatment of procrastination is the analysis of the design opportunities for hybrid apps that provide hedonic and eudaimonic experiences while simultaneously integrating professional therapeutic guidance.

Although procrastination represents a significant self-regulatory failure with the potential to cause adverse mental and physical health consequences, the rate of adherence to professional therapies remains low, particularly in light of the considerable number of individuals affected. Specific forms of BCSSs, namely, motivational BCSSs that elicit positive experiences through hedonic and eudaimonic design elements, have been proposed as a promising self-treatment alternative for individuals struggling with procrastination. However, research on the design and effects of such systems is still in its infancy. Conversely, a multitude of motivational behavior change support apps designed to facilitate behavior change and overcome procrastination have already been developed and implemented in practice. To analyze these practical developments, a systematic review and cluster analysis of prevalent smartphone apps was conducted, resulting in the identification of 5 motivational design archetypes. Upon critical discussion, these archetypes appeared to target different psychological determinants of procrastination and successfully use a variety of hedonic and eudaimonic design elements that extend beyond the current state of research. Nevertheless, further investigation is warranted to ascertain how hedonic and eudaimonic design can be more effectively integrated with cognitive BCTs and how social support mechanisms that have demonstrated efficacy in traditional procrastination treatment can be elicited through motivational BCSSs.