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Regional differences in thermal sensation to a hot or cold stimulus are often limited to male participants, in a rested state and cover minimal locations. Therefore, magnitude sensation to both a hot and cold stimulus were investigated during rest and exercise in 8 females (age: 20.4 ± 1.4 years, mass: 61.7 ± 4.0 kg, height: 166.9 ± 5.4 cm, VO2max: 36.8 ± 4.5 ml·kg(-1)·min(-1)). Using a repeated measures cross over design, participants rested in a stable environment (22.3 ± 0.9°C, 37.7 ± 5.5% RH) whilst a thermal probe (25 cm(2)), set at either 40°C or 20°C, was applied in a balanced order to 29 locations across the body. Participants reported their thermal sensation after 10s of application. Following this, participants cycled at 50% VO2max for 20 min and then 30% VO2max whilst the sensitivity test was repeated. Females experienced significantly stronger magnitude sensations to the cold than the hot stimulus (5.5 ± 1.7 and 4.3 ± 1.3, p<0.05, respectively). A significant effect of location was found during the cold stimulation (p<0.05). Thermal sensation was greatest at the head then the torso and declined towards the extremities. No significant effect of location was found in response to the hot stimulation and the pattern across the body was more homogenous. In comparison to rest, exercise caused a significant overall reduction in thermal sensation (5.2 ± 1.5 and 4.6 ± 1.7, respectively, p<0.05). Body maps were produced for both stimuli during rest and exercise, which highlight sensitive areas across the body.

Background Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions. Objective The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume. Methods We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy. Results Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines. Conclusions Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions.