The study explored the feasibility of applying a clustering method to develop a knowledge-base, which might be a first step towards more personalized suggestions on the content level in future digital health interventions. More specifically, this study investigated whether user information was related to specific action and coping plans. The results can be readily summarized. First, we were able to cluster action plans, coping plans and the combination of action and coping plans. Second, relating these clusters to user information was possible for action plans, but proved more difficult for the coping plans and specific combination of action and coping plans.
Our study revealed that some user characteristics related toward specific action plans. 1) Users with a higher BMI were more likely to choose outdoor leisure activities (walking, biking, running). 2) Women, users that did not perform PA regularly yet, or users who had a job, were more likely to choose for household activities. 3) Younger users were more likely to choose for active transport and different sports activities (fitness, swimming, tennis). Of these younger adults, users with a higher BMI were more likely to choose for active transport whereas users with a lower BMI would choose for different sports or work-related activities. Overall, these findings suggest that with the approach used in this study, it is feasible to find relations between action plans and specific personal user information. Consequently, the knowledge acquired from these findings might be used to define relationships in a knowledge-base and to ultimately personalize suggestions for action plans.
Although we could identify relatively consistent pairs of barriers and solutions formulated in the clusters of coping plans, no meaningful link was found between user information and coping plans or specific combinations of action and coping plans. This might be due to the fact that 8 clusters were identified for both coping plans and for specific combinations of action and coping plans. This is a relatively large number of clusters to relate user information to and may explain why, although statistical differences were found, these differences were not straightforward to interpret. Moreover, clusters of the specific combinations of action plans and coping plans were not considered to be valuable because it was difficult to meaningfully distinguish one cluster from another. Advanced clustering techniques with a larger and more heterogeneous sample may identify more valuable clusters in future research (29). Also, the reason for the large numbers of clusters might reflect the fact that it is not possible to cluster coping plans or specific combinations of action and coping plans. In that case, only suggestions of action plans could be formulated rather than suggestions of coping plans or combinations of both. Another, maybe more important explanation, might be that the current paper only analyzed personal user information (i.e. demographic information and motivational stage). Consequently, relationships of other user information with plan characteristics remain unexplored. As it is known that PA is not a stable but dynamic (i.e. time-dependent) behaviour that varies throughout the day and from day to day (30, 31), it is more likely that barriers to certain physical activities relate more to contextual (e.g. the weather) and dynamic user information (e.g. emotions) than personal and rather stable user information (e.g. demographic information). Thus, future research should investigate whether more contextual and dynamic user information relates more strongly towards plan characteristics.
Considering the above findings, the clustering method used in the current study might be a feasible approach to acquire knowledge for a knowledge-base. However more user information will be needed to deliver personalized suggestions on the content level. First, we will illustrate how the results of the current study might be used to develop a knowledge-base, and then we will discuss what other user information might be needed to deliver more context-aware personalized suggestions. To develop a knowledge-base, acquired knowledge should be organized into a structure. Ontologies are one of the most popular approaches to structure these knowledge-bases as they are well specified (32), and can be combined with intelligent algorithms, which makes it possible to deliver personalized suggestions (e.g. for example, a user who does not perform PA regularly yet may get a suggestion to do a household activity). Furthermore, Larsen (32) shows that ontologies are already increasingly used by behavioural scientists. Indeed, several ontologies in the PA and behaviour change domain already exist (32). For example, the Physical Activity Concept Ontology (PACO) structures different physical activities (33), the HAPA ontology structures all constructs of the HAPA-model (34), the Behaviour Change Interventions Ontology (BCIO) is a broader ontology that structures knowledge about interventions, their contexts, effects and evaluations (35, 36). Even though ontologies such as the BCIO and HAPA ontology provide structures and their relationships on an abstract construct level (e.g. ‘intention’ influences ‘planning’, ‘planning’ positively influences the 'Intenders' to 'Actors' transitions relationship (34)), they still lack detailed concretization of these constructs and their interrelations at the content level (32). Yet, ontologies with detailed concretizations at the content level are needed to deliver context-aware personalized suggestions. The current paper provides an approach to acquire knowledge for such an ontology. Here follows an example of how an ontology with the findings from the current paper may be used to deliver personalized suggestions. Suppose Rosy (older woman, higher BMI, high education, pre-intender for PA) logs into an m-health intervention. Which suggestions could the system deliver to get her more active the following week? Since the ontology contains relationships between user information and plan characteristics, the system can exploit the ontology with the help of intelligent algorithms and deduce which specific plan suggestions would match Rosy’s user profile. Based on the findings of the current paper, the suggestions ‘do a household activity’ or ‘do an outdoor activity such as walking, biking or running’ could be delivered to Rosy. In addition, Larsen (32) highlights the importance of combining ontologies with other ontologies in the field and asks the scientific community to update ontologies as new evidence emerges. As such, findings from the current paper could take other ontologies (such as the BCIO or the HAPA ontology) to a higher level by adding detailed concretizations at the content level. Consequently, the approach used in this study may contribute to the refinement of ontologies related to behavior change interventions.
As previously stated, more user information in relation to plan characteristics should be acquired in order to develop a knowledge-base for context-aware personalized suggestions. Future studies might use the same approach of the current study to acquire more data from possible end users to shape the knowledge-base. However, the current approach should be enriched with other user information that might be placed on two continuums: First, relatively stable user information (i.e. information that does not change over a certain period of time) versus more dynamic user information (i.e. information that varies over a certain period of time). Second, personal user information (i.e. information related to the individual itself) versus contextual user information (i.e. information related to the context of the individual). The current paper addressed demographic info and motivational stage as stable and personal user information. Other personal and relatively stable user information worth investigating might be (perceived) motor skill competence or physical health. For example, recent research of Drenowatz (37) showed that a higher motor skill competence could be linked to more club sports participation. However these findings were only identified in children (37). Another example shows that patients with chronic back pain perform more physical activities in the morning than in the evening compared to controls (38). Second, exploring whether relatively stable and contextual user information relates to plan characteristics might provide important information as well. It might be interesting to explore whether users from various home or work environments make different plans or encounter different barriers to do PA. For example, research already showed that neighborhoods supporting a safe, enjoyable and social experience are associated with more leisure time walking among adults (39). Third, as PA is not a static but dynamic behaviour (30, 31), it would be useful to examine whether certain dynamic personal user information (e.g. emotions, fatigue, pain) and dynamic contextual user information (e.g. weather, agenda of the day) relates to certain plan characteristics. This would enable the knowledge-base to deliver personalized suggestions based not only on relatively ‘stable information’ but also on ‘dynamic information’. For example, when the user has a busy day at work (dynamic contextual info), he might need other suggestions than when a user has more time; or when a user is stressed (dynamic personal info), the user might need other suggestions than when a user is more relaxed that day.
Some considerations should be taken into account for future research. To acquire more dynamic information, future studies should collect data on a smaller timeframe (day to day or even within days) as compared to the current study (only once at the start of the study). Relatively stable information might still be collected at the start of the study and/or at another moment depending on the study length (e.g. motivational stage may change after 3 weeks in an intervention). To acquire more dynamic information, ecological momentary assessment (EMA) might be used because this method makes it possible to collect real-time data based on repeated measures and observations that take place in participant’s daily environment (40). For instance, during a 7-day EMA study the emotional state of a participant might be asked for example every three to four hours, together with the question to make a PA plan for these following hours. This example would make it possible to relate certain emotions toward specific plans.
If a knowledge-base were to be developed based on the approach of the current study and the above-mentioned suggestions, future interventions to promote PA can exploit the knowledge-base in order to deliver context-aware personalized suggestions. Notwithstanding, these future interventions might take further steps toward context-aware personalized suggestions. First, despite the fact that more information in the knowledge-base may result in more context-aware personalized suggestions, one should be careful with asking too many questions at the start of a personalized intervention (in order to determine the new user’s profile). Using smart technologies such as wearables and apps (e.g. to measure stress, PA level, location, agenda, weather) could limit the number of questions. Second, the current study highlights the importance of a knowledge-base to deliver context-aware personalized suggestions in e-and m-health interventions. Nonetheless, it is unlikely that this approach will fully capture the complexity of behaviour change to provide context-aware personalized suggestions. Other approaches could complement the approach used in the current study. One of these approaches is ‘reinforcement learning’ (41). In its most basic level, the system learns by measuring a success criterion for a given suggestion: if the success criterion is met, the probability of suggesting this suggestion a second time increases (41). For example, the success criterion can be based on the user’s rating for a certain suggestion, or the user’s behaviour after that suggestion (e.g. if a user gets a suggestion of a plan to go for a walk and the user eventually goes for a walk). More advanced derivations of reinforcement learning should be explored in future interventions, for example success criterions of similar users (41, 42). Third, another approach that might complement the current approach is the systems ID approach. The approach used in this study is still a ‘nomothetic’ approach (i.e. making ‘aggregated’ conclusions of relationships of user info and plan characteristics), whereas ‘ideographic approaches’ might deliver more context-aware personalized suggestions (i.e. making individualized conclusions of relationships between user info and plan characteristics by examining within-person variation over time). The systems ID approach is an ‘ideographic approach’ and learns from run-in periods to provide personalized suggestions (e.g. for a certain user it might be better to suggest a walking activity on a weekend day, whereas for another user it might be better to suggest a walking activity on a weekday) (30, 41). The disadvantage of such a run-in period is that no context-aware personalized suggestions can be delivered at the beginning of such an intervention (which is also the case when using reinforcement learning on its own).
Strengths and limitations
This study has several strengths. First, the current study demonstrated a proof-of-concept (clustering method) which provides insights in how a DSs with a knowledge-base could be developed in order to deliver more context-aware personalized suggestions in future digital interventions. Until now, many studies use a black-box approach in which details about how support is generated in the DSs are unknown (43). Furthermore, the few available studies that did employ DSs lack information on the use of behaviour change theories (42). Second, acquired knowledge in knowledge-bases in previous studies is mostly expert driven (23, 24), whereas the current study was theory-driven and data-driven. This approach gave us the opportunity to get more insights in comparison with only expert knowledge. Nonetheless, we urge caution when using clustering algorithms on their own (e.g. giving suggestions of household activities only to women, may reinforce standard, normative and/or stereotypical patterns of behaviour). Therefore, expert consultation remains important.
This study also has a number of limitations. First, the user sample for the current study was small, clustering with action plans and coping plans of a larger and more heterogeneous sample will possibly give better insights. Second, this study focused on which plans users created in order to do PA, we did not measure the actual performance of the plan. Investigating user information in relation to performing actual PA may also provide useful insights for personalized suggestions (e.g. if a user is feeling stressed, what kind of physical activities does the user perform?). Future studies may also use EMA to collect this data. Third, the current study demonstrated proof-of-concept to acquire knowledge for a knowledge-base in order to provide more personalized suggestions on the content level, however it is not clear whether this approach will be more effective to promote PA than simpler tailoring approaches (e.g. tailoring on construct level, tailoring based on preferences of the individual). Future research might investigate which approaches are most effective to promote PA. Finally, the focus here was whether user information related toward specific action and coping plans, in order to deliver personalized suggestions of these plans on the content level. Future studies might also consider other BCTs, such as self-monitoring (e.g. older users maybe relate to other self-monitoring methods than younger users) or outcome-expectancies (e.g. when someone is stressed that person might need another message to see the advantage of PA than when someone is relaxed).