Evidence map›Paper›PMID 42812338›Full record

Observational studyFrontiers in public health2026

Predicting low muscle strength from routine fitness-monitoring data in hypertensive older adults living at high altitude: development and internal validation of interpretable models.

Ting Chen, Xin Zhao, Ruizhe Sun, Ba Bian, Weibao Liang

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In one paragraph

Observational study in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ting ChenKey Laboratory of Plateau Hypoxia Environment and Life and Health, School of Physical Education, Plateau Sports ad Health Research Center, Xizang Minzu University, Xianyang, China.
Xin ZhaoTibet University for Nationalities Affiliated Hospital, Xianyang, China.
Ruizhe SunTibet Autonomous Region Institute of Sports Science, Lhasa, China.
Ba BianMedical College, Tibet University, Lhasa, China.
Weibao LiangFaculty of Health Sciences and Sports, Macao Polytechnic University, Macao, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Low muscle strength based on grip-strength thresholds is a readily measurable marker of impaired muscle function, yet it is under-recognized among hypertensive older adults at high altitude. We developed a concurrent clinical prediction model using routine fitness-monitoring data and tested whether the full feature set added information beyond sex, age, and site/altitude. Methods: This cross-sectional secondary analysis included 433 hypertensive adults aged 60-69 years from three sites in the National Physical Fitness Monitoring program in the Tibet Autonomous Region. Low muscle strength was defined using Asian Working Group for Sarcopenia 2019 grip-strength thresholds (men <28 kg; women <18 kg). Fifteen predictors were prespecified for routine availability and plausible relevance to body composition, cardiometabolic status, respiratory or neuromotor function, physical activity, and site/altitude. Elastic-net logistic regression, random forest, and histogram gradient boosting were evaluated by repeated nested cross-validation. Results: Low muscle strength was present in 171/433 participants (39.5%). Random forest had the highest AUC point estimate (0.788, 95% CI 0.742-0.831), but its interval overlapped with elastic-net logistic regression (0.781, 0.732-0.825) and histogram gradient boosting (0.774, 0.729-0.819). Elastic-net was designated as the candidate model because calibration was closest to ideal (slope 0.99, 95% CI 0.78-1.24; intercept -0.00, -0.22 to 0.24) and it provides a transportable equation. Its PR-AUC was 0.725 versus a no-skill baseline of 0.395. Incremental discrimination over sex plus age plus site/altitude was small (ΔAUC 0.016, 95% CI - 0.009 to 0.041). AUC was 0.705 without sex, 0.653 under a sex-specific tertile outcome, and 0.626-0.759 when each site was held out. Conclusion: Routine fitness-monitoring variables contain information about concurrent low muscle strength, but much of the apparent performance is explained by the sex-specific outcome definition and site/altitude structure. Direct grip-strength measurement remains preferable whenever feasible. The elastic-net model should be regarded only as a candidate for opportunistic triage when a valid grip measurement is unavailable or unusable; independent external validation, recalibration, and prospective impact evaluation are required before implementation.

Indexed as

AltitudeClinical Decision RulesHypertensionPrediction Methods, MachineSarcopeniaAgedFemaleHand StrengthHumansMaleMiddle AgedPhysical FitnessReproducibility of ResultsTibetclinical prediction modelhandgrip strengthhigh-altitude residencehypertensionlow muscle strengthmachine learningolder adults

Identifiers

PMID42812338
PMCPMC13619500

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.