Jose M. Saavedra

The demand for sleep interventions is high and steadily growing. Digital therapeutics (DTx) can help individuals improve their sleep remotely, over an extended period, and with less effort from medical professionals. Obstructive sleep apnea (OSA), one of the most prevalent and consequential sleep disorders, can be treated with health-supporting behavior changes, such as physical exercise and weight loss, and, therefore, acts as a promising application for DTx. The study aimed to analyze a digital intervention from both medical and technological perspectives by moving beyond clinical markers and exploring more deeply how the DTx application was used. This study aimed to propose a novel way in which association rules can function as an exploratory tool to analyze the sleep, behavior, and engagement of participants with the DTx application on a day-to-day level. A lifestyle intervention study (N=192) targeted at adults with mild-to-moderate OSA aimed to reduce their OSA severity using a DTx application and an exercise program over a study period of 12 weeks. The participants' OSA severity was assessed through polysomnography at the beginning and at the end of the study period, and the participants tracked their sleep with a digital sleep diary and a smartwatch over the course of the entire study. The DTx application provided data on when and how the participants pursued the proposed lifestyle interventions. These heterogeneous data sources were combined into one multimodal dataset, which was explored through descriptive statistics. Ultimately, the data were turned into a transaction-based format, and association rules were derived using the Apriori algorithm. Analyzing the participants' interaction with the application revealed the lifestyle interventions they pursued and how their behavior and sleep patterns changed over time. The Apriori algorithm generated a set of association rules with lift and confidence scores that were significantly higher than those for the co-occurrence of items through random chance. The rules show co-occurrence of missions and items from the sleep diary, as well as items derived from the watch measurements. The study showed the richness of the various data sources provided by a digital intervention using wearables and how they can be used to get an in-depth understanding of the study. The generated association rules showed the presence of significant co-occurrences across the different data modalities and highlighted their effectiveness as an exploratory tool for multimodal health data.

Background: Previous research has attempted to determine the minimum number of days of accelerometry required to reliably reflect an individual’s physical activity. However, human behaviors on a day-to-day basis can be highly variable. As a consequence, the number of days required to reliably predict habitual physical activity is dependent on the variability that exists within an individual. There is a concern that adopting generic recommendations from previous research could provide unreliable estimates by failing to represent individuals with specific physical activity patterns. Objective: The main aim of this study was to identify clusters of individuals with distinct physical activity patterns and to determine if the number of days of accelerometry data required to reliably estimate short- (7 days) and medium-term (28 days) physical activity differed between each unique cluster. Methods: Accelerometry data were retrieved from 2 independent research studies. Participants during each study had their physical activity recorded using a Withings Scanwatch (Withings Health Solutions). Following a data eligibility process, agglomerative hierarchical clustering was used to identify clusters of individuals based on their physical activity. The clusters were determined using 4 dimensions; mean, SD, skewness, and kurtosis of the step count data. Intraclass correlation coefficients (ICCs) of step count were then calculated within each physical activity cluster. A series of ICCs were computed by separately comparing the average step count across the full periods (7 and 28, for the short- and medium-term analysis, respectively) to a series of averaged subsamples (ranging from 1-6 days and 1-27 days, for the short- and medium-term analysis, respectively). For each subsample, 500 random combinations were generated and compared, providing a distribution of ICCs for each subsample. An ICC of ≥0.80 identified when the subsample of days was sufficient to achieve appropriate reliability. Results: Of 258 participant datasets, 149 were eligible for the short-term analysis and 64 were eligible for the medium-term analysis. Following agglomerative hierarchical clustering, 4 and 3 clusters of sufficient size (n≥12) were identified in the short-term and medium-term analyses, respectively. When considering the short-term analysis, to achieve a mean ICC score greater than or equal to 0.80, using all randomized combinations, the number of days ranged from 2 to 6 days depending on the physical activity cluster. For the medium-term analysis, the number of days required to achieve a mean ICC score greater than or equal to 0.80 ranged from 6 to 11 days. The short-term analysis clusters displayed more diversity in physical activity patterns than the medium-term analysis. Conclusions: Physical activity patterns influence the number of days required to estimate habitual physical activity. Thus, to avoid unreliable estimates of physical activity, which could significantly impact the interpretation of results, researchers should be mindful of the physical activity patterns of their sample before adopting generic recommendations.

Background: The aim of this article is to describe the protocol of a large, multicenter randomized controlled trial evaluating the effects of orofacial myofunctional therapy with auto-monitoring (OMTa) versus auto-monitoring alone on obstructive sleep apnea (OSA) assessed by the apnea–hypopnea index and other pre-specified outcomes. Method: The OMTaOSA study protocol was registered at ClinicalTrials.gov (NCT06079073) in August 2023, and data collection ended in January 2025. One hundred and four participants with mild and moderate OSA were included. Randomization was conducted in a 1:1 ratio, using sex-stratified blocks. The intervention was a standardized protocol of OMT exercises previously shown to be effective, auto-monitoring with a Withings scan watch, and feedback from self-reports. Controls received watches and access to the same application without the exercise module. Sleep was measured over three nights at baseline and after three months. The sleep scorer and researchers evaluating other outcomes were blinded to the treatment allocation. Change in the apnea–hypopnea index was defined as the primary outcome. Secondary outcomes are published on Clinicaltrials.gov. Results: The results of the trial are still in preparation. Conclusions: By addressing the limitations of previous OMT studies, this trial may clarify the effectiveness of digitally delivered OMT for patients with mild to moderate OSA.

Increased use of analytics in sports has shed some light on various suboptimal strategies that have long been considered the norm. While sports such as basketball and baseball have adapted to a changing landscape, handball seems slow to adapt. The aim of this study was to examine i) the prevalence of high- and low-probability shots in semi-professional handball, ii) whether taking high-probability shots was associated with winning and iii) whether there were any differences between the men's and women's leagues. Six seasons in the Icelandic elite division were analyzed using a mixed-effects logistic regression model (1410 games, 879 games in the men's league and 531 games in the women's league). The study found that low-probability shots outnumbered high-probability shots (54.1% to 45.9%) and that high-probability shots were associated with winning (odds ratio of 1.46, compared to odds ratio of 0.73 in the low-probability shots). This was especially pronounced in the women's league (odds ratio of 1.77, compared to an odds ratio of 1.20 in the means league). These findings suggest that some handball teams could enhance their performance by focusing on creating high-probability shots. However, the success of such efforts depends not only on the attacking team's tactics but also on the defensive strategies employed by their opponents, which significantly influence the quality of scoring opportunities. Ultimately, the interplay between offensive and defensive approaches dictates the frequency and feasibility of high-probability shots.

Objectives. The objectives of this study were (i) to design in detail an exercise program for individuals with sleep-disordered breathing (SDB) that would be reproducible, and (ii) to present a system for monitoring training load (volume × intensity) within such a program. Methods. A comprehensive exercise program was developed for individuals with SDB, detailing not only the session structure (warm-up, main part—circuit training and brisk walking—and cool-down) but also the specific exercises, training volume (actual exercise time excluding rest), intensity (Borg Rating of Perceived Exertion—RPE), and training load (calculated as time × RPE, in arbitrary units). This detailed program was previously implemented in a RCT (ISRCTN16974764). A comparison was also made between the planned and performed intensity, and training load through a paired t-test. Results. A fully replicable program was presented. No significant difference was found between the planned and performed training load (p = 0.482). When analyzed by week, a significant difference was found only for overestimation in weeks 9–12 (p < 0.001). Conclusions. In general terms, it can be concluded that a detailed exercise program was described for individuals with SDB. The program is reproducible in terms of content, training volume, intensity, and load. Moreover, the RPE proved to be a valid parameter for quantifying intensity, allowing for the integration of all parts of the session, as well as various types of content. The planned and performed programs (as quantified via participants’ RPE) matched appropriately. Therefore, this program can be reproduced and applied to this type of population.

BACKGROUND Previous research has attempted to determine the minimum number of days of accelerometry required to reliably reflect an individual’s physical activity. However, human behaviors on a day-to-day basis can be highly variable. As a consequence, the number of days required to reliably predict habitual physical activity is dependent on the variability that exists within an individual. There is a concern that adopting generic recommendations from previous research could provide unreliable estimates by failing to represent individuals with specific physical activity patterns. OBJECTIVE The main aim of this study was to identify clusters of individuals with distinct physical activity patterns and to determine if the number of days of accelerometry data required to reliably estimate short- (7 days) and medium-term (28 days) physical activity differed between each unique cluster. METHODS Accelerometry data were retrieved from 2 independent research studies. Participants during each study had their physical activity recorded using a Withings Scanwatch (Withings Health Solutions). Following a data eligibility process, agglomerative hierarchical clustering was used to identify clusters of individuals based on their physical activity. The clusters were determined using 4 dimensions; mean, SD, skewness, and kurtosis of the step count data. Intraclass correlation coefficients (ICCs) of step count were then calculated within each physical activity cluster. A series of ICCs were computed by separately comparing the average step count across the full periods (7 and 28, for the short- and medium-term analysis, respectively) to a series of averaged subsamples (ranging from 1-6 days and 1-27 days, for the short- and medium-term analysis, respectively). For each subsample, 500 random combinations were generated and compared, providing a distribution of ICCs for each subsample. An ICC of ≥0.80 identified when the subsample of days was sufficient to achieve appropriate reliability. RESULTS Of 258 participant datasets, 149 were eligible for the short-term analysis and 64 were eligible for the medium-term analysis. Following agglomerative hierarchical clustering, 4 and 3 clusters of sufficient size (n≥12) were identified in the short-term and medium-term analyses, respectively. When considering the short-term analysis, to achieve a mean ICC score greater than or equal to 0.80, using all randomized combinations, the number of days ranged from 2 to 6 days depending on the physical activity cluster. For the medium-term analysis, the number of days required to achieve a mean ICC score greater than or equal to 0.80 ranged from 6 to 11 days. The short-term analysis clusters displayed more diversity in physical activity patterns than the medium-term analysis. CONCLUSIONS Physical activity patterns influence the number of days required to estimate habitual physical activity. Thus, to avoid unreliable estimates of physical activity, which could significantly impact the interpretation of results, researchers should be mindful of the physical activity patterns of their sample before adopting generic recommendations.

Alcohol drinking is an important health-related problem and one of the major risk factors for a wide array of non-communicable diseases, while there is a lack of studies investigating environment-specific associations between sports participation and alcohol drinking in adolescence. This study prospectively investigated the relationship between sports factors (i.e., participation in sports and competitive achievement), with the prevalence of harmful alcohol drinking (HD), and HD initiation in 14-to-16 years old adolescents from Bosnia and Herzegovina (n = 641, 337 females, 43% living in rural community). Participants were tested over 4-time points divided by approximately 6 months, from the beginning of high school to the end of the second grade. Variables included gender, factors related to sport participation, a community of residence (urban or rural), and outcome: alcohol consumption was assessed by the AUDIT questionnaire. Results evidenced that the prevalence of HD increased over the study period from 6 to 19%, with no significant differences between urban and rural youth. Logistic regression for HD as criterion evidenced adolescents who participated in sports and then quit as being at particular risk for drinking alcohol at the study baseline. Sports factors were not correlated with HD initiation in the period between 14 and 16 years of age. It seems that the problem of alcohol drinking should be preventively targeted in all youth, irrespective of living environment. Although sports participation was not evidenced as being a factor of influence on HD initiation, results highlight the necessity of developing targeted preventive campaigns against alcohol drinking for adolescents who quit sports.

It is widely recognized that there is a decline in match running performance (MRP) towards the end of matches. To clarify whether it is primarily a consequence of fatigue, pacing or situational influences, this study aimed to examine MRP across 15-min match periods for players on different playing positions. Players’ MRP (n = 244) were examined from the UEFA Champions League matches (n = 20) using a semiautomatic optical tracking system. Linear mixed models for repeated measures were adjusted to analyze MRP over the six 15-min match periods while controlling the influence of situational factors. No effects of match outcome, match location, team, and opponent quality on total distance (TD) and high-intensity running (HIR) for players in all playing positions were found (F = 0.03–2.75; all p > 0.05). Significant differences in TD (F = 17.57–53.01; η2 = 0.39–0.52, all large effect sizes) and HIR (F = 3.67–7.64; η2 = 0.05–0.19, small to medium effect sizes) among six 15-minute match periods were found for players in all playing positions. In addition, players in all playing positions covered less TD (d = 1.41–2.15, large to very large effect sizes) and HIR (d = 0.16–0.6, trivial to medium effect sizes) in the last compared to the first 15-min match period. No differences in TD and HIR between the last two match periods in the second half were observed. This study confirmed that soccer players reduce MRP towards the end of matches, and suggest that the decline of MPR in highest-level soccer may be a consequence of pacing strategies.