Data-driven meal events detection using blood glucose response patterns

BACKGROUND

In the Diabetes domain, events such as meals and exercises play an important role in the disease management. For that, many studies focus on automatic meal detection, specially as part of the so-called artificial [Formula: see text]-cell systems. Meals are associated to blood glucose (BG) variations, however such variations are not peculiar to meals, it mostly comes as a combination of external factors. Thus, general approaches such as the ones focused on glucose signal rate of change are not enough to detect personalized influence of such factors. By using a data-driven individualized approach for meal detection, our method is able to fit real data, detecting personalized meal responses even when such external factors are implicitly present.

METHODS

The method is split into model training and selection. In the training phase, we start observing meal responses for each individual, and identifying personalized patterns. Occurrences of such patterns are searched over the BG signal, evaluating the similarity of each pattern to each possible signal subsequence. The most similar occurrences are then selected as possible meal event candidates. For that, we include steps for excluding less relevant neighbors per pattern, and grouping close occurrences in time globally. Each candidate is represented by a set of time and response signal related qualitative variables. These variables are used as input features for different binary classifiers in order to learn to classify a candidate as MEAL or NON-MEAL. In the model selection phase, we compare all trained classifiers to select the one that performs better with the data of each individual.

RESULTS

The results show that the method is able to detect daily meals, providing a result with a balanced proportion between detected meals and false alarms. The analysis on multiple patients indicate that the approach achieves good outcomes when there is enough reliable training data, as this is reflected on the testing results.

CONCLUSIONS

The approach aims at personalizing the meal detection task by relying solely on data. The premise is that a model trained with data that contains the implicit influence of external factors is able to recognize the nuances of the individual that generated the data. Besides, the approach can also be used to improve data quality by detecting meals, opening opportunities to possible applications such as detecting and reminding users of missing or wrongly informed meal events.

Evaluating the Impact of Adaptive Personalized Goal Setting on Engagement Levels of Government Staff With a Gamified mHealth Tool: Results From a 2-Month Randomized Controlled Trial

Background

Although the health benefits of physical activity are well established, it remains challenging for people to adopt a more active lifestyle. Mobile health (mHealth) interventions can be effective tools to promote physical activity and reduce sedentary behavior. Promising results have been obtained by using gamification techniques as behavior change strategies, especially when they were tailored toward an individual’s preferences and goals; yet, it remains unclear how goals could be personalized to effectively promote health behaviors.

Objective

In this study, we aim to evaluate the impact of personalized goal setting in the context of gamified mHealth interventions. We hypothesize that interventions suggesting health goals that are tailored based on end users’ (self-reported) current and desired capabilities will be more engaging than interventions with generic goals.

Methods

The study was designed as a 2-arm randomized intervention trial. Participants were recruited among staff members of 7 governmental organizations. They participated in an 8-week digital health promotion campaign that was especially designed to promote walks, bike rides, and sports sessions. Using an mHealth app, participants could track their performance on two social leaderboards: a leaderboard displaying the individual scores of participants and a leaderboard displaying the average scores per organizational department. The mHealth app also provided a news feed that showed when other participants had scored points. Points could be collected by performing any of the 6 assigned tasks (eg, walk for at least 2000 m). The level of complexity of 3 of these 6 tasks was updated every 2 weeks by changing either the suggested task intensity or the suggested frequency of the task. The 2 intervention arms—with participants randomly assigned—consisted of a personalized treatment that tailored the complexity parameters based on participants’ self-reported capabilities and goals and a control treatment where the complexity parameters were set generically based on national guidelines. Measures were collected from the mHealth app as well as from intake and posttest surveys and analyzed using hierarchical linear models.

Results

The results indicated that engagement with the program inevitably dropped over time. However, engagement was higher for participants who had set themselves a goal in the intake survey. The impact of personalization was especially observed for frequency parameters because the personalization of sports session frequency did foster higher engagement levels, especially when participants set a goal to improve their capabilities. In addition, the personalization of suggested ride duration had a positive effect on self-perceived biking performance.

Conclusions

Personalization seems particularly promising for promoting the frequency of physical activity (eg, promoting the number of suggested sports sessions per week), as opposed to the intensity of the physical activity (eg, distance or duration). Replications and variations of our study setup are critical for consolidating and explaining (or refuting) these effects.

Trial Registration

ClinicalTrials.gov NCT05264155; https://clinicaltrials.gov/ct2/show/NCT05264155

Pilot Evaluation of the Impact of Lottery-Based Incentives on Engagement Levels of Male Low SES Vocational Students With an mHealth App

In general, individuals with lower socioeconomic status (SES) are less physically active and adhere to poorer diets than higher SES individuals. To promote healthier lifestyles in lower SES populations, we hosted a digital health promotion program among male vocational students at a school in The Netherlands. In a pilot study, we evaluated whether this target audience could be engaged with an mHealth app using lottery-based incentives that trigger feelings of anticipated regret. Especially, we studied the social and interpersonal aspects of regret lotteries in a within-subject experimental design. In this design, subjects either participated in a social variant (i.e., with students competing against their peers for a chance at a regret lottery), or an individual variant (i.e., with subjects solely individually engaged in a lottery). Additionally, we studied the impact of different payout schedules in a between-subject experimental design. In this design, participants were assigned to either a short-term, low-value payout schedule, or a long-term, high-value payout schedule. From a population of 72 male students, only half voluntarily participated in our 10-week program. From interviews, we learned that the main reason for neglecting the program was not related to the lottery-based incentives, nor to the prizes that were awarded. Instead, non-enrolled subjects did not join the program, because their peers were not joining. Paradoxically, it was suggested that students withheld their active participation until a larger portion of the sample was actively participating. From the subjects that enrolled in the program (N = 36, males, between 15 and 25 years of age), we found that a large proportion stopped interacting with the program over time (e.g., after roughly 4 weeks). Our results also indicated that students performed significantly more health-related activities when assigned to the social regret lottery, as opposed to the individual variant. This result was supported by interview responses from active participants: They mainly participated to compete against their peers, and not so much for the prizes. Hence, from this study, we obtained initial evidence on the impact of social and competitive aspects in lottery-based incentives to stimulate engagement levels in lower SES students with an mHealth app.

Assessing the Influence of Physical Activity Upon the Experience Sampling Response Rate on Wrist-Worn Devices

The Experience Sampling Method (ESM) is gaining ground for collecting self-reported data from human participants during daily routines. An important methodological challenge is to sustain sufficient response rates, especially when studies last longer than a few days. An obvious strategy is to deliver the experiential questions on a device that study participants can access easily at different times and contexts (e.g., a smartwatch). However, responses may still be hampered if the prompts are delivered at an inconvenient moment. Advances in context sensing create new opportunities for improving the timing of ESM prompts. Specifically, we explore how physiological sensing on commodity-level smartwatches can be utilized in triggering ESM prompts. We have created Experiencer, a novel ESM smartwatch platform that allows studying different prompting strategies. We ran a controlled experiment (N=71) on Experiencer to study the strengths and weaknesses of two sampling regimes. One group (N=34) received incoming notifications while resting (e.g., sedentary), and another group (N=37) received similar notifications while being active (e.g., running). We hypothesized that response rates would be higher when experiential questions are delivered during lower levels of physical activity. Contrary to our hypothesis, the response rates were found significantly higher in the active group, which demonstrates the relevance of studying dynamic forms of experience sampling that leverage better context-sensitive sampling regimes. Future research will seek to identify more refined strategies for context-sensitive ESM using smartwatches and further develop mechanisms that will enable researchers to easily adapt their prompting strategy to different contextual factors.

Preadolescent Students' Engagement With an mHealth Intervention Fostering Social Comparison for Health Behavior Change: Crossover Experimental Study

Background

Contemporary mobile health (mHealth) interventions use various behavior change techniques to promote healthier lifestyles. Social comparison is one of the techniques that is consensually agreed to be effective in engaging the general population in mHealth interventions. However, it is unclear how this strategy can be best used to engage preadolescents. Nevertheless, this strategy has great potential for this target audience, as they are particularly developing their social skills.

Objective

This study aims to evaluate how social comparison drives preadolescents’ engagement with an mHealth app.

Methods

We designed a 12-week crossover experiment in which we studied 3 approaches to implementing behavior change via social comparison. This study was hosted in a school environment to leverage naturally existing social structures among preadolescents. During the experiment, students and teachers used an mHealth tool that awarded points for performing healthy activities. Participants could read their aggregated scores on a leaderboard and compare their performance with others. In particular, these leaderboards were tweaked to implement 3 approaches of the social comparison technique. The first approach focused on intragroup comparison (ie, students and teachers competing against each other to obtain the most points), whereas the other two approaches focused on intergroup comparison (ie, classes of students and their mentoring teachers collaborating to compete against other classes). Additionally, in the third approach, the performance of teachers was highlighted to further increase students’ engagement through teachers’ natural exemplary function. To obtain our results, we used linear modeling techniques to analyze the dropout rates and engagement levels for the different approaches. In such analyses, we also considered individual participant traits.

Results

Our sample included 313 participants—290 students (92.7%) and 23 teachers (7.3%). It was found that student engagement levels dropped over time and declined during holidays. However, students seemed to monitor the intergroup competitions more closely than the intragroup competitions, as they, on average, checked the mHealth app more often when they were engaged in team-based comparisons. Students, on average, performed the most unique activities when they were engaged in the second intergroup setting, perhaps because their teachers were most active in this setting. Moreover, teachers seemed to play an important role in engaging their students, as their relationship with their students influenced the engagement of the students.

Conclusions

When using social comparison to engage preadolescents with an mHealth tool, an intergroup setting, rather than an intragroup competition, motivated them to engage with the app but did not necessarily motivate them to perform more activities. It seems that the number of unique activities that preadolescents perform depends on the activeness of a role model. Moreover, this effect is amplified by preadolescents’ perceptions of closeness to that role model.

Unified Health Gamification can significantly improve well-being in corporate environments

There is a multitude of mHealth applications that aim to solve societal health problems by stimulating specific types of physical activities via gamification. However, physical health activities cover just one of the three World Health Organization (WHO) dimensions of health. This paper introduces the novel notion of Unified Health Gamification (UHG), which covers besides physical health also social and cognitive health and well-being. Instead of rewarding activities in the three WHO dimensions using different mHealth competitions, UHG combines the scores for such activities on unified leaderboards and lets people interact in social circles beyond personal interests. This approach is promising in corporate environments since UHG can connect the employees with intrinsic motivation for physical health with those who have quite different interests. In order to evaluate this approach, we realized an app prototype and we evaluated it in two corporate pilot studies. In total, eighteen pilot users participated voluntarily for six weeks. Half of the participants were recruited from an occupational health setting and the other half from a treatment setting. Our results suggest that the UHG principles are worth more investigation: various positive health effects were found based on a validated survey. The mean mental health improved significantly at one pilot location and at the level of individual pilot participants, multiple other effects were found to be significant: among others, significant mental health improvements were found for 28% of the participants. Most participants intended to use the app beyond the pilot, especially if it would be further developed.

A meta-model for computer executable dynamic clinical safety checklists

BACKGROUND

Safety checklist is a type of cognitive tool enforcing short term memory of medical workers with the purpose of reducing medical errors caused by overlook and ignorance. To facilitate the daily use of safety checklists, computerized systems embedded in the clinical workflow and adapted to patient-context are increasingly developed. However, the current hard-coded approach of implementing checklists in these systems increase the cognitive efforts of clinical experts and coding efforts for informaticists. This is due to the lack of a formal representation format that is both understandable by clinical experts and executable by computer programs.

METHODS

We developed a dynamic checklist meta-model with a three-step approach. Dynamic checklist modeling requirements were extracted by performing a domain analysis. Then, existing modeling approaches and tools were investigated with the purpose of reusing these languages. Finally, the meta-model was developed by eliciting domain concepts and their hierarchies. The feasibility of using the meta-model was validated by two case studies. The meta-model was mapped to specific modeling languages according to the requirements of hospitals.

RESULTS

Using the proposed meta-model, a comprehensive coronary artery bypass graft peri-operative checklist set and a percutaneous coronary intervention peri-operative checklist set have been developed in a Dutch hospital and a Chinese hospital, respectively. The result shows that it is feasible to use the meta-model to facilitate the modeling and execution of dynamic checklists.

CONCLUSIONS

We proposed a novel meta-model for the dynamic checklist with the purpose of facilitating creating dynamic checklists. The meta-model is a framework of reusing existing modeling languages and tools to model dynamic checklists. The feasibility of using the meta-model is validated by implementing a use case in the system.

Aligning Event Logs to Task-Time Matrix Clinical Pathways in BPMN for Variance Analysis

Clinical pathways (CPs) are popular healthcare management tools to standardize care and ensure quality. Analyzing CP compliance levels and variances is known to be useful for training and CP redesign purposes. Flexible semantics of the business process model and notation (BPMN) language has been shown to be useful for the modeling and analysis of complex protocols. However, in practical cases one may want to exploit that CPs often have the form of task-time matrices. This paper presents a new method parsing complex BPMN models and aligning traces to the models heuristically. A case study on variance analysis is undertaken, where a CP from the practice and two large sets of patients data from an electronic medical record (EMR) database are used. The results demonstrate that automated variance analysis between BPMN task-time models and real-life EMR data are feasible, whereas that was not the case for the existing analysis techniques. We also provide meaningful insights for further improvement.