Nutrition Table 1 Dietary Intake in Individuals with SCI

Author
Year
Country
Sample Size

Methods

Outcomes

Sabour et al. 2016
Iran
Observational
N=103

Population: Mean age: 39.5 yr; Gender: males=86, females=17; Injury etiology: unspecified; Level of injury: cervical=23, thoracic=63, lumbar=17; Level of severity: AIS A=76, B=13, C=4, D=10.
Intervention:
Participants were assessed upon admission to a research centre.
Outcome Measures:
Caloric Intake, Protein Intake, Body Mass Index (BMI), Bone Mineral Density (BMD).

  1. Measurements were taken at the femoral neck (FN), femoral trochanter (FT), femoral intertrochanteric zone (FIZ), lumbar vertebrae (LV), and hip.
  2. BMD was significantly correlated with BMI at all measured points (p<0.05).
  3. BMD was significantly greater in female participants at all measured points (p<0.05), except at the FN.
  4. BMD of the LV was significantly greater in participants with incomplete injury (p<0.05) and with paraplegia (p<0.05).
  5. BMD of the FIZ was significantly greater in participants with AIS D (p<0.05).
  6. Caloric intake was not significantly correlated with BMD at any point.
  7. Protein intake was negatively correlated with BMD of the LV (r=­0.24, p=0.03).
  8. BMD of the LV was negatively correlated with intake of tryptophan, isoleucine, lysine, cysteine, tyrosine, threonine, leucine, methionine, phenylalanine, valine, and histidine (p<0.05).

Gorgey et al. 2015
USA
Observational
N=16

Population: Mean age: 38 yr; Gender: males=16, females=0; Injury etiology: unspecified; Level of injury: C5-7=6, T3-10=10; Level of severity: AIS A=12, B=4; Time since injury: >1yr.
Intervention:
Participants from the community were assessed and dietary intake was recorded for 4wk.
Outcome Measures:
Dietary Record Frequency, Percentage of Macronutrients, Caloric Intake, Total Energy Expenditure (TEE), Basal Metabolic Rate (BMR), Fat-Free Mass (FFM), Fat Mass (FM).

  1. Caloric intake decreased over 4 wk, but the difference was not significant (p=0.056). There was no significant difference (p=0.93) or interaction (p=0.54) in measuring caloric intake among different dietary record frequencies (1, 3, or 5 d/wk).
  2. TEE was significantly higher than caloric intake using 1 d (p=0.001), 3 d (p=0.015), or 5 d (p=0.005) dietary frequency records.
  3. BMR was not significantly different from caloric intake for any dietary record frequency, and the two were not significantly correlated.
  4. BMR was significantly correlated with total FFM (r=0.71, p=0.005), leg FFM (r=0.55, p=0.04), and trunk FFM (r=0.62, p=0.018).
  5. Percentage of macronutrients consumed was not significantly different among dietary frequency records: fat (p=0.92), carbohydrates (p=0.50), or protein (p=0.35).
  6. Percentage of fat consumed was significantly different across 4 wk (p=0.031), particularly at 2-3 wk (p=0.034). There was no significant interaction among dietary record frequencies in measuring fat intake (p=0.80).
  7. Percentage of carbohydrates consumed was significantly different across 4 wk (p=0.032), particularly at 1-3 wk (p=0.026) and 2-3 wk (p=0.014). There was no significant interaction among dietary record frequencies in measuring carbohydrate intake (p=0.30).
  8. Percentage of protein consumed was significantly different across 4 wk (p=0.021), particularly at 1-3 wk (p=0.008). There was no significant interaction among dietary record frequencies in measuring protein intake (p=0.025).
  9. Percentage of fat consumed accounted for 29% of total FM (r2=0.29, p=0.037), 34% of leg FM (r2=0.34, p=0.022), and 24% of trunk FM (r2=0.24, p=0.066). It was negatively correlated with total FFM (r=­0.53, p=0.04), trunk FFM (r=­0.54, p=0.036), and BMR (r=­0.52, p=0.059).
  10. Percentage of carbohydrates was negatively correlated with % fat (r=­0.92, p<0.0001), % protein (r=­0.67, p=0.005), total FM (r=­0.56, p=0.031), leg FM (r=­0.64, p=0.01), and trunk FM (r=­0.50, p=0.059). It was positively correlated with total FFM (r=0.54, p=0.037) trunk FFM (r=0.52, p=0.046), and BMR (r=0.55, p=0.04).
  11. Percentage of protein was not correlated with FM, FFM, or BMR.

Tsunoda et al. 2015
Japan
Observational
N=841

Population: Mean age: 61 yr; Gender: males=718, females=123; Injury etiology: unspecified; Level of injury: cervical=245, thoracic=434, lumbar=162; Level of severity: unspecified; Mean time since injury: 27 yr.
Intervention: Participants from the community were assessed via questionnaires, and categorized as superior (n=413) or subordinate (n=428) based on food intake score.
Outcome Measures: Food Intake, Trans-Theoretical Model (TTM), Self-Efficacy (SE), Outcome Expectancy (OE).
  1. Food intake frequency scores between the superior and subordinate groups were significantly different in age (p<0.001), gender (p=0.002), living situation (p=0.002), and care services status (p=0.007).
  2. In univariate analysis, all food intake variables were significantly correlated (p<0.001) with TTM (OR range: 2.55-5.89) SE (OR range: 1.93-4.08), and OE (OR range: 1.61-2.76).
  3. In multivariate analysis, TTM was significantly correlated with the following food intake variables: ‘to eat vegetable dishes’ (OR=2.76, p<0.001), ‘to eat green/yellow vegetables (OR=2.29, p=0.003), ‘to eat dairy products’ (OR=2.75, p<0.001), and ‘to eat fruits’ (OR=1.87, p=0.003).
  4. In multivariate analysis, SE was significantly correlated with the following food intake variables: ‘to eat vegetable dishes’ (OR=2.12, p=0.008), ‘to eat dairy products’ (OR=1.91, p=0.001), and ‘to eat fruits’ (OR=1.97, p=0.001).
  5. In multivariate analysis, OE was not significantly correlated with any food intake variable.

Lieberman et al. 2014
USA
Observational
N=100

Population: Mean age: 45.3 yr; Gender: males=78, females=22; Injury etiology: unspecified; Level of injury: paraplegia=43, quadriplegia=57; Level of severity: AIS A=66, B=16, C=18; Mean time since injury: 15.1 yr;
Intervention: Participants from the community were assessed and compared to age- and gender-matched controls (n=100).
Outcome Measures: Nutrient Intake, Food Intake, Dietary Guideline Adherence.
  1. Nutrient intake: participants consumed significantly less calcium (means: 1049 versus 1415 mg; p=0.004) and Vitamin D (means: 223 versus 315 IU; p=0.009) when compared to controls.
  2. Food intake: participants consumed significantly fewer mean daily servings of dairy (2.10 versus 4.79, p<0.0001), fruit (2.01 versus 3.64, p=0.002), whole grains (1.20 versus 2.44, p=0.007), and sugars (1.46 versus 3.50, p=0.002) when compared to controls.
  3. Guidelines: fewer participants adhered to recommended daily servings of fruits and vegetables (>5 cups; 40.3% versus 68.7%, p<0.001), whole grains (>3 oz; 8.9% versus 21.1%, p=0.01), and dairy (>3 cups; 23.4% versus 48.6%, p<0.001).

Wong et al. 2014
UK
Observational
N=150

Population: Median age: 44 yr; Gender: males=46, females=104; Injury etiology: trauma=107, non-trauma=43; Level of injury: cervical=57, thoracic=59, lumbar=22, sacral=1; Level of severity: AIS A=70, B=10, C=28, D=31; Mean time since injury: unspecified.
Intervention: Participants were assessed upon admission to SCI centers.
Outcome Measures: Spinal Nutrition Screening Tool (SNST), Malnutrition Universal Screening Tool (MUST), Length of Stay (LOS), Mortality.
  1.  6% of participants were at risk for undernutrition (SNST>11 / MUST>1).
  2. LOS was significantly higher in at-risk participants than those not at risk (129 versus 85 d, p=0.012).
  3. Increased LOS was associated with higher SNST score (p=0.012), higher MUST score (p=0.013), new admission (p<0.01), prior ITU stay (p<0.01), low protein (p=0.022), low albumin (p<0.01), and weight loss >10% (p<0.01).
  4. Mortality rate at 1 yr was significantly higher in at-risk participants than those not at risk (10.2% versus 1.4%, p=0.036).
  5. Higher mortality was associated with age >60 yr (p<0.01), readmission (p=0.018), pressure ulcers (p=0.028), and mechanical ventilation (p=0.025).
  6. In univariate analyses, predictors of LOS were SNST score (p=0.003), MUST score (p=0.003), injury level (p=0.027), admission type (p<0.001), mechanical ventilation usage (p=0.003), prior ITU stay (p<0.001), serum protein (p=0.002), and serum albumin (p<0.001).
  7. In multivariate analysis, predictors of LOS were admission type (B=81.23, p<0.001) and serum albumin (B=­3.62, p=0.013).

Pellicane et al. 2013
USA
Observational
N=78

Population: SCI (n=16): Mean age=41.1±21.2 yr; Gender: males=13, females=3; Level of injury: tetraplegia=8, paraplegia=8; Other injury etiologies: TBI=9, stroke=43, Parkinson’s disease (PD)=10.
Treatment: Rehabilitation inpatients were assessed by a Registered Dietician for dietary intake once weekly.
Outcome Measures: Calorie and protein intake. 

  1. Total calorie intake was significantly higher in individuals with SCI compared to stroke (p<0.003) and PD (p<0.45).
  2. Calorie intake per body weight (cal/kg) was significantly higher in individuals with SCI compared to stroke (p<0.025).
  3. There were no significant differences in total protein intake between varying etiologies.
  4. Age (p<0.001), gender (p=0.023), were significant predictors of calorie and protein intake; admission weight also predicted calorie intake (p=0.025).

Krempien & Barr 2012
Canada
Observational
N=32

Population: Mean age: 30.6 yr; Gender: males=24, females=8; Injury etiology: unspecified; Level of injury: paraplegia=12, quadriplegia=20; Level of severity: unspecified; Time since injury: unspecified.
Intervention: Participants with professional athletic history were assessed.
Outcome Measures: Three-Factor Eating Questionnaire (TFEQ), Body Mass Index (BMI), Sum of Skinfolds (SoS), Dietary Intake.
  1. Participants with low dietary restraint (<11; n=16) had significantly lower TFEQ disinhibition score (2.1 versus 3.5, p<0.05) and percentage of energy from protein (16.9% versus 18.4%, p<0.05) than those with high dietary restraint.
  2. There were no significant differences in BMI, SoS, or other dietary intakes (i.e. calories, carbohydrates, fat, fibre) between high and low dietary restraint groups.
  3. TFEQ dietary restraint score was not significantly associated with BMI, SoS, or dietary intakes (p>0.05).
  4. TFEQ disinhibition score was significantly associated with SoS (r=0.513, p=0.003).
  5. TFEQ hunger score was significantly associated with intake of calories (r=0.354, p=0.047), carbohydrates (r=0.361, p=0.042), and protein (r=0.456, p=0.009).

Sabour et al. 2012
Iran
Observational
N=162

Population: Mean age=34.2±0.7 yr; Gender: males=131, females=31; Level of injury: tetraplegia=94, paraplegia=68; Time since injury=8.0±0.5 yr.
Treatment: Face-to-face interviews examining habitual daily food intake patterns.
Outcome Measures: Macronutrient intake, simple carbohydrate intake,  total calorie intake.

  1. Percentages of total energy intake derived from macronutrients were 53% vs. 52% carbohydrate, 10% vs. 11% protein, and 37% vs. 39% for men and women, respectively.
  2. There was excessive consumption of simple carbohydrates (102.2±40.4 g/d).
  3. Males consumed a greater number of calories than women (p<0.05).
  4. No difference in total intake between those with tetraplegia versus paraplegia.
  5. Individuals with incomplete injuries consumed significantly more monounsaturated fatty acids than those with complete injuries (p=0.03).
  6. Age, education and gender significantly predictor calories intake; time since injury, education, and gender were significant predictors for carbohydrate intake.
  7. Smoking and level of injury were not related to any dietary variable, and there were no significant predictors for dietary protein and simple carbohydrate.

Wong et al. 2012
UK
Observational
N=150

Population: Age: <60 yr=109, >60 yr=38; Level of injury: C=41.1%, T=42.4%, L=15.8%, S=0.7%; Severity of injury: AIS A=50.4%, B=7.2%, C=20.1%, D=22.3%.
Treatment: Assessment of nutritional risk on admission to SCI centers.
Outcome Measures: Malnutrition Universal Screening Tool, body mass index (BMI).

  1. At the time of hospital admission, 40.0% of the sample were found to be nutritionally ‘at risk’ and 21.4% were assessed as being ‘at high risk’ of malnutrition.
  2. The highest prevalence of nutritional risk was found in groups with prior intensive care unit stays (p=0.035), mechanical ventilation (p=0.183) and artificial nutritional support at the time of arrival (<0.001).
  3. Nutritional risk showed no significant difference with increased age (p=0.913).
  4. Compared with ‘no-risk’ patients, at-risk patients were found to have significantly lower concentrations of total protein, albumin, Hb, creatinine and Mg, with lower BMI and less appetite.
  5. ‘At-risk’ patients were found to be receiving more prescribed medications.
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