Helén, J, Kyröläinen, H, Ojanen, T, Pihlainen, K, Santtila, M, Heikkinen, R, and Vaara, JP. High-intensity functional training induces superior training adaptations compared with traditional military physical training. J Strength Cond Res 37(12): 2477–2483, 2023—This study examined the effectiveness of concurrent strength and endurance training with an emphasis on high-intensity functional training (HIFT) during military service. Voluntary male conscripts (aged 18–28 years) were placed in either an experimental (EXP: n = 50–66) or a control (CON: n = 50–67) group. The training for the EXP group included HIFT using body mass, sandbags, and kettlebells. The CON group trained according to the current practice. Physical performance and body composition were assessed at baseline (PRE), at week 10 (MID), and after (POST) the 19-week training period. Significance was set at p < 0.05. The total distance covered in a 12-minute running test increased in both groups, but the change in EXP was superior to the change in CON (11.6%, ES: 0.79 vs. 5.7%, ES: 0.33; p = 0.027). Maximal strength and power characteristics increased in EXP (3.1–5.0%), whereas no improvements were observed in CON. Conscripts with the highest initial fitness showed no improvements in physical performance in either group. Body mass and waist circumference decreased in EXP, whereas CON showed an increase in muscle mass. These findings suggest that HIFT is an effective and time-efficient approach to improve soldiers' aerobic fitness during military service. For the optimal development of strength, the training equipment used may not have provided sufficient and progressive loading to yield considerable strength adaptations. More focus should be placed on sufficient intensity and volume in both strength and endurance training, especially for the most fit soldiers.

Background: In the military context, high levels of physiological and psychological stress together can compromise individual’s ability to complete given duty or mission and increase dropout rates. The purpose of this study was to investigate if baseline physical fitness, body composition, hormonal and psychological factors could predict dropout from a 10-day intense winter military survival training. Methods: 69 conscripts volunteered to participate in the study. Physical fitness (muscle strength and power, muscle endurance, and aerobic fitness), body composition and hormonal variables (BDNF, testosterone, cortisol, SHBG, DHEAS, IGF-1) together with self-reported psychological factors (short five personality, hardiness, sense of coherence, stress, depression) were assessed prior the survival training. Results: During the survival training, 20 conscripts (29%) dropped out. Baseline aerobic fitness (hazard ratio, HR: 0.997, 95% CI: 0.994–0.999, p = 0.006) and serum cortisol (HR: 1.0006, 95% CI: 1.001–1.011, p = 0.017) predicted dropout in Cox regression model. Each 10 m increase in the 12 min running test decreased the risk for dropout by 3%. Conclusion: Although most of the physiological and psychological variables at the baseline did not predict dropout during a short-term winter survival military training, baseline information of aerobic fitness and serum cortisol concentration may be useful to target support for individuals at higher potential risk for dropout.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

We introduce a Bayesian stable isotope mixing model for estimating the relative contributions of different dietary components to the tissues of consumers within food webs. The model is implemented with the probabilistic programming language Stan. The model incorporates isotopes of multiple elements (e.g. C, N, H) for two trophic levels, when the structure of the food web is known. In addition, the model allows inclusion of latent trophic levels (i.e. for which no empirical data are available) intermediate between sources and measured consumers. Running the model in simulations driven by a real dataset from Finnish lakes, we tested the sensitivity of the posterior distributions by altering critical prior parameters and assumptions in the data‐generating process. Importantly, we found that the model estimations were particularly sensitive to the assigned prior value for ω (the fraction of H in aquatic consumer tissue that is derived from environmental water rather than diet) so that reliable empirical data for this parameter are required. When reliable information is not available for ω, we suggest that an uninformative prior should be used. The proposed model and inferences are suitable for studies where resources for collecting new data are limited, but useful prior information for each specific trophic level is available from earlier studies.

Background: Results from studies on the effects of exercise on smoking-related variables have provided strong evidence that physical activity acutely reduces cigarette cravings. Mobile technology may provide some valuable tools to move from explanatory randomized controlled trials to pragmatic randomized controlled trials by testing the acute effectiveness of exercise on quitters under real-life conditions. An mHealth app was developed to be used as a support tool for quitters to manage their cigarette cravings. Objective: The primary aim of this paper is to present the protocol of a study examining the effectiveness of the Physical over smoking app (Ph. o. S) by comparing the point prevalence abstinence rate of a group of users to a comparator group during a 6-month follow-up period. Methods: After initial Web-based screening, eligible participants are recruited to attend a smoking cessation program for 3 weeks to set a quit smoking date. Fifty participants who succeed in quitting will be randomly allocated to the comparator and experimental groups. Both groups will separately have 1 more counseling session on how to manage cravings. In this fourth session, the only difference in treatment between the groups is that the experimental group will have an extra 10-15 minutes of guidance on how to use the fully automated Ph. o. S app to manage cravings during the follow-up period. Data will be collected at baseline, as well as before and after the quit day, and follow-up Web-based measures will be collected for a period of 6 months. The primary efficacy outcome is the 7-day point prevalence abstinence rate, and secondary efficacy outcomes are number of relapses and cravings, self-efficacy of being aware of craving experience, self-efficacy in managing cravings, and power of control in managing cravings. Results: Recruitment for this project commenced in December 2014, and proceeded until May 2015. Follow-up data collection has commenced and will be completed by the end of December 2015. Conclusions: If the Ph. o. S app is shown to be effective, the study will provide evidence for the use of the app as a support tool for people who are trying to manage cravings during smoking cessation programs. It is anticipated that the results of the study will provide knowledge of how physical activity affects cigarette craving in real-life situations and inform the development and delivery of relapse prevention in smoking cessation treatment.