Physical fitness characteristics and neck and shoulder pain incidence in school‐aged children—A 2‐year follow‐up

Pirnes, Katariina Pauliina; Kallio, Jouni; Hakonen, Harto; Hautala, Arto J.; Joensuu, Laura; Häkkinen, Arja; Tammelin, Tuija

doi:10.1002/hsr2.852

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Physical fitness owned by students varies. The characteristics of good physical fitness in children can consistently be seen from the child's ability to carry out activities without feeling fatigue (Pirnes et al, 2022). The importance of physical fitness starts from children by doing activities and maintaining a pattern of lifestyle and eating well, physical fitness as an important health marker in youth (Popovici et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Body mass index and physical fitness level of elementary school students

Allsabah,

Putra,

Sugito

2023

Edu Sportivo: Indonesian j. of Physical Education

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The fitness condition of elementary school students is of particular concern in general, as today's technological advances and sedentary lifestyles greatly affect body mass index and physical fitness. This study aimed to determine the body mass index and physical fitness of elementary school students aged 10–12 years. The research method used in this study is a survey with a test approach and measurement of body condition. The subjects of this study used a population sampling technique by taking 1115 students from elementary schools in Kediri City and Regency. Research instruments used in the 2010 Indonesian Physical Fitness Test for children aged 10–12 years with 5 tests, namely 40 metres of running, bent elbow hanging test, sit-up test, upright jump test, vertical jump test, and 600-metre running test Measurement of body mass index (BMI) criteria in children aged 5–18 years based on height and weight owned by students The results of this study showed that male and female students in elementary schools in Kediri City and Regency got the results of the Body Mass Index (BMI) with a normal classification. The physical fitness results of male students in elementary schools in Kediri City and Regency show physical fitness results owned by sons with good classification. The physical fitness results of female students in elementary schools in Kediri City and Regency showed that most classifications were average. This study concludes that BMI and physical fitness must be maintained and improved. In the future, based on the results of this research, policies and methods will be needed so that BMI and student fitness will be even better.

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Section: Introductionmentioning

confidence: 99%

Body mass index and physical fitness level of elementary school students

Allsabah,

Putra,

Sugito

2023

Edu Sportivo: Indonesian j. of Physical Education

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“…Findings remain persistently inconclusive, although the majority of studies have focused on examining a specific pain site. 16,17 Despite the inconclusive evidence, proper functioning of the musculoskeletal system, i.e adequate exertion of force, fatigue resistance, and range of motion in the body, is a rationale for many health-related large-scale monitoring and surveillance systems to implement fitness testing at the population level. 16,18 Previous findings also indicate that girls report pain more often than boys, 2,3 and the correlates of pain might be sex-specific, potentially due to differences in maturation, pain tolerance, or coping behaviors between sexes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of multisite pain incidence in adolescence using a machine learning approach

Joensuu

Rautiainen

Hautala

et al. 2023

Preprint

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Importance Multisite pain is a major adverse health outcome in the adolescent population, affecting the daily lives of up to every third adolescent and their families. Objective To 1) predict multisite pain incidence in the whole body and in the musculoskeletal locations in adolescents, and 2) explore the sex-specific predictors of multisite pain incidence with a novel machine learning approach. Design A 2-year observational study (2013-2015). Three different baseline data sets were utilized to predict multisite pain incidence during the follow-up. Setting Population-based sample of Finnish adolescents. Participants Apparently healthy adolescents. Exposures The first data set included 48 selected baseline variables relevant for adolescents health and wellbeing. Data included information on students self-reported, objectively measured, and device-based demographics, physical and psychosocial characteristics, and lifestyle factors. The second data set included nine physical fitness variables related to the Finnish national Move! monitoring and surveillance system for health-related fitness. The third data set included all available baseline data (392 variables). Main Outcome and Measures Onset of multisite pain (=weekly pain during the past three months manifesting in at least three sites and not related to any known disease or injury) during the 2-year follow-up in the whole body or musculoskeletal locations. Musculoskeletal pain sites included the neck/shoulder, upper extremities, chest, upper back, low back, buttocks, and lower extremities. Whole body pain sites also the head and abdominal areas. A machine learning algorithm random forest was utilized. Results Among 410 participants (57% girls) aged on average 12.5-years (SD 1.2), sixteen percent of boys and 28.1% of girls developed multisite pain in the whole body and 9.6% and 15.3% in the musculoskeletal area during follow-up. The prediction ability of the machine learning approach with 48 predictive variables reached an AUC 0.65 at highest. With ML, a broad variety of predictors were identified, with up to 33 variables showing predictive power in girls and 13 in boys. Conclusions and Relevance Findings highlight that rather than any isolated variable, a variety of factors pose a risk for future multisite pain. More emphasis on holistic and multidisciplinary approaches is recommended to prevent multisite pain in adolescence.

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Prediction of Multisite Pain Incidence in Adolescence Using a Machine Learning Approach: A 2‐Year Longitudinal Study

Joensuu,

Rautiainen,

Hautala

et al. 2024

Health Science Reports

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Background and AimsMultisite pain is a prevalent and significant issue among adolescents, often associated with adverse physical, psychological, and social outcomes. We aimed to (1) predict multisite pain incidence in the whole body and in the musculoskeletal sites in adolescents, and (2) explore the sex‐specific predictors of multisite pain incidence using a novel machine learning (ML) approach (random forest, AdaBoost, and support vector classifier).MethodsA 2‐year longitudinal observational study (2013–2015) was conducted in a population‐based sample of Finnish adolescents (N = 410, 57% girls, 12.5 years (SD = 1.2) at baseline). Three different data sets were used. First data included 48 pre‐selected variables relevant for adolescents' health and wellbeing. The second data included nine physical fitness variables related to the Finnish national ‘Move!’ monitoring system for health‐related fitness. The third data set included all available baseline data (392 variables). Multisite pain was self‐reported weekly pain during the past 3 months manifesting in at least three sites and not related to any known disease or injury. Musculoskeletal pain sites included the neck/shoulder, upper extremities, chest, upper back, low back, buttocks, and lower extremities. Whole body pain sites also included the head and abdominal areas.ResultsOverall, 16% of boys and 28% of girls developed multisite pain in the whole body and 10% and 15% in the musculoskeletal area during the 2‐year follow‐up. The prediction ability of ML reached area under the receiver operating characteristic curve 0.78 at highest but remained mainly < 0.7 for the majority of the methods. With ML, a broad variety of predictors were identified, with up to 33 variables showing predictive power in girls and 13 in boys.ConclusionThe results highlight that rather than any isolated variable, a variety of factors contribute to future multisite pain.

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Physical fitness characteristics and neck and shoulder pain incidence in school‐aged children—A 2‐year follow‐up

Cited by 3 publications

References 30 publications

Body mass index and physical fitness level of elementary school students

Body mass index and physical fitness level of elementary school students

Prediction of multisite pain incidence in adolescence using a machine learning approach

Prediction of Multisite Pain Incidence in Adolescence Using a Machine Learning Approach: A 2‐Year Longitudinal Study

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