Management of nut allergy influences quality of life and anxiety in children and their mothers

Nut allergy is known to impact on the quality of life (QoL) and anxiety of both the allergic child and their parents, but little is known about how the management of food allergy is associated with these variables. To investigate the impact of nut allergy on QoL and anxiety in mothers and children with nut allergy in order to identify management strategies that may influence these factors. Forty-one nut allergic children (age 6–16 yrs) and their mothers completed questionnaires to assess maternal and children’s QoL (PedsQL™, WHOQOL-BREF, FAQL-PB), anxiety (SCAS, STAI) and perceived stress scale (PSS). Children also completed a nut allergy specific QoL questionnaire. Demographic data, details of previous reactions, test results and management plans were collected using parent-report questionnaires and hospital notes. Children with nut allergy had poorer emotional (p = 0.004), social (p = 0.043), and psychological (p = 0.006) QoL compared to healthy normative data. Maternal and child QoL and anxiety were not influenced by the severity of previous reactions. Mother and child reported lower anxiety (p = 0.043 and p < 0.001 respectively) when the child was prescribed an epinephrine auto-injector. Anxiety was not associated with whether the child carried the auto-injector or whether they strictly avoided traces of nuts in foods. Prescribing auto-injectors is associated with reduced anxiety for food allergic children and their mothers, but is not associated with improved adherence with medical management or reduced risk-taking behavior.

The psychological impact of diagnostic food challenges to confirm the resolution of peanut or tree nut allergy

Background - Twenty percent of children outgrow peanut allergy and 10% outgrow tree nut allergy. Resolution can be confirmed by a food challenge. Little is known about the psychosocial impact of the challenge. We aimed to investigate effects of a food challenge on anxiety, stress and quality of life (QoL) in children and their mothers on the day of a food challenge to peanuts or nuts, and in the months following the challenge. Methods - One hundred and three families participated. Forty children undergoing food challenges to access resolution of allergy, and their mothers, completed validated questionnaires to measure generic and food specific quality of life, stress and anxiety prior to challenge, on the day of investigation and 3–6 months later. Sixty-three children with no clinical indication to challenge (i.e. in the opinion of the allergist had persistent allergy) acted as comparison group completing questionnaires 3–6 months apart. Results - Mothers reported raised anxiety on the day of challenge (P = 0.007), but children were less anxious. The children (P = 0.01) and mothers (P = 0.01) had improved food-related, but not general, QoL 3–6 months following challenge. Children reported lower anxiety levels following the challenge (P = 0.02), but anxiety remained unchanged in mothers. The improvements in maternal and children's QoL and anxiety levels were irrespective of the challenge outcome and despite co-existing food allergies in 50% of children. Conclusions - Mothers experienced increased anxiety on the day of food challenge, unlike the children, perhaps reflecting the differences in their perceived risks. Food challenges are associated with improved food-related QoL in the following months even in those with a positive challenge.

Using genetic programming and decision trees for generating structural descriptions of four bar mechanisms

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.