Substantiating the Use of Tendotonometry for the Assessment of Achilles and Patellar Tendon Stiffness: A Systematic Review.

OBJECTIVE: To systematically describe the next relevant aspects of tendotonometry in (1) its validity and reliability, (2) differences between populations, (3) the effect of interventions, and (4) differences between healthy and symptomatic Achilles tendon (AT) and patellar tendon (PT). DATA SOURCES: Three online databases (PubMed, Embase, and EBSCOhost) were systematically searched on the 10th of October 2023. All scientific literature concerning the use of tendotonometry in assessing tendon stiffness was collected. Articles were eligible if tendotonometry with a myotonometer digital palpation device was used to assess PT or AT stiffness in adults. MAIN RESULTS: Thirty-four studies were included, which were categorized into studies regarding the (1a) reliability and (1b) validity of tendotonometry, (2) differences in stiffness between populations, (3) changes in stiffness due to interventions, (4) stiffness of healthy compared with injured tendons, and (5) other observational studies. The inter-rater and intrarater reliability of tendotonometry appeared to be good in assessing AT and PT stiffness, with only moderate evidence for the AT and inconclusive evidence for the PT. There is high certainty evidence that tendotonometry can detect differences in AT and PT stiffness after training interventions. Inconsistent results were found for the adequacy of tendotonometry to detect differences in AT and PT stiffness between populations. CONCLUSIONS: This review shows a potential role for tendotonometry in measuring tendon stiffness. However, more research is needed for validating the use of tendotonometry in AT and PT and its exact clinical interpretation.

Dietary intake of heme iron is associated with ferritin and hemoglobin levels in Dutch blood donors: results from Donor InSight.

Whole blood donors, especially frequently donating donors, have a risk of iron deficiency and low hemoglobin levels, which may affect their health and eligibility to donate. Lifestyle behaviors, such as dietary iron intake and physical activity, may influence iron stores and thereby hemoglobin levels. We aimed to investigate whether dietary iron intake and questionnaire-based moderate-to-vigorous physical activity were associated with hemoglobin levels, and whether ferritin levels mediated these associations. In Donor InSight-III, a Dutch cohort study of blood and plasma donors, data on heme and non-heme iron intake (mg/day), moderate-to-vigorous physical activity (10 minutes/day), hemoglobin levels (mmol/L) and ferritin levels (µg/L) were available in 2,323 donors (1,074 male). Donors with higher heme iron intakes (regression coefficients (ß) in men and women: 0.160 and 0.065 mmol/L higher hemoglobin per 1 mg of heme iron, respectively) and lower non-heme iron intakes (ß: -0.014 and -0.017, respectively) had higher hemoglobin levels, adjusted for relevant confounders. Ferritin levels mediated these associations (indirect effect (95% confidence interval) in men and women respectively: 0.074 (0.045; 0.111) and 0.061 (0.030; 0.096) for heme and -0.003 (-0.008;0.001) and -0.008 (-0.013;-0.003) for non-heme). Moderate-to-vigorous physical activity was negatively associated with hemoglobin levels in men only (ß: -0.005), but not mediated by ferritin levels. In conclusion, higher heme and lower non-heme iron intake were associated with higher hemoglobin levels in donors, via higher ferritin levels. This indicates that donors with high heme iron intake may be more capable of maintaining iron stores to recover hemoglobin levels after blood donation.

Prevalence and determinants of declining versus stable hemoglobin levels in whole blood donors.

BACKGROUND: A too short recovery time after blood donation results in a gradual depletion of iron stores and a subsequent decline in hemoglobin (Hb) levels over time. This decline in Hb levels may depend on individual, unobserved characteristics of the donor. STUDY DESIGN AND METHODS: We used a data set of 5388 Dutch blood donors from the Donor InSight study. The statistical analysis is based on a Bayesian growth mixture model, which assumes that each donor belongs to one of several groups. Each group implies a different Hb trajectory, and donors with similar longitudinal trajectories belong to the same group. Analyses were performed for male and female donors separately. RESULTS: For both sexes the model identified four groups of donors. Stable Hb trajectories were found among 14% of male donors and 15% of female donors; declining Hb trajectories were observed in the remaining groups of donors. The percentage of donor deferrals differed strongly between groups. CONCLUSION: The model can be used to predict to which group a donor belongs, and this prediction can be updated after each donation. This is of high practical importance because early identification of donors with declining Hb levels could help to tailor donation intervals and to prevent iron deficiency and donor deferrals.

Predicting hemoglobin levels in whole blood donors using transition models and mixed effects models.

BACKGROUND: To optimize the planning of blood donations but also to continue motivating the volunteers it is important to streamline the practical organization of the timing of donations. While donors are asked to return for donation after a suitable period, still a relevant proportion of blood donors is deferred from donation each year due to a too low hemoglobin level. Rejection of donation may demotivate the candidate donor and implies an inefficient planning of the donation process. Hence, it is important to predict the future hemoglobin level to improve the planning of donors' visits to the blood bank. METHODS: The development of the hemoglobin prediction rule is based on longitudinal (panel) data from blood donations collected by Sanquin (the only blood product collecting and supplying organization in the Netherlands). We explored and contrasted two popular statistical models, i.e. the transition (autoregressive) model and the mixed effects model as plausible models to account for the dependence among subsequent hemoglobin levels within a donor. RESULTS: The predictors of the future hemoglobin level are age, season, hemoglobin levels at the previous visits, and a binary variable indicating whether a donation was made at the previous visit. Based on cross-validation, the areas under the receiver operating characteristic curve (AUCs) for male donors are 0.83 and 0.81 for the transition model and the mixed effects model, respectively; for female donors we obtained AUC values of 0.73 and 0.72 for the transition model and the mixed effects model, respectively. CONCLUSION: We showed that the transition models and the mixed effects models provide a much better prediction compared to a multiple linear regression model. In general, the transition model provides a somewhat better prediction than the mixed effects model, especially at high visit numbers. In addition, the transition model offers a better trade-off between sensitivity and specificity when varying the cut-off values for eligibility in predicted values. Hence transition models make the prediction of hemoglobin level more precise and may lead to less deferral from donation in the future.