Development of a tool for palliative care needs assessment and intervention: mixed methods research at a Swiss tertiary oncology clinic.

BACKGROUND: Palliative care interventions improve quality-of-life for advanced cancer patients and their caregivers. The frequency and quality of service provision could be improved by a clinical tool that helps oncology professionals to assess unmet needs for palliative care interventions and to structure the interventions delivered. This paper aims to answer the following research question: what do oncology professionals and cancer patients view as important elements in a clinical tool for assessing unmet palliative care needs? Based on the feedback from professionals and patients, we developed and refined an intervention-focused clinical tool for use in cancer care. METHODS: This study used a prospective convergent mixed methods design and was carried out at a single tertiary hospital in Switzerland. Healthcare professionals participated in focus groups (n=29) and a Delphi survey (n=73). Patients receiving palliative care were interviewed (n=17). Purposive sampling was used to achieve maximal variation in participant response. Inductive content analysis and descriptive statistics were used to analyze focus group discussions, open-ended survey questions and interview data. Descriptive statistics were used for analyzing quantitative survey items and interviewee characteristics. RESULTS: Focus groups and Delphi surveys showed that seven key palliative care interventions were important to oncology professionals. They also valued a tool that could be used by doctors, nurses, or other professionals. Participants did not agree about the best timepoint for assessment. Two versions of a pilot clinical tool were tested in patient interviews. Interviews highlighted the divergent patient needs that must be accommodated in clinical practice. Patients provided confirmation that a clinical tool would be helpful to them. CONCLUSIONS: This paper reports on research carried out to understand what elements are most important in a tool that helps oncology professionals to identify patients' unmet needs and provide tailored palliative care interventions. This study demonstrated that professionals and patients alike are interested in a clinical tool. Responses from oncology healthcare professionals helped to identify relevant palliative care interventions, and patients provided constructive input used in designing a tool for use in clinical interactions.

Energy Expenditure and Hormone Responses in Humans After Overeating High-Fructose Corn Syrup Versus Whole-Wheat Foods.

OBJECTIVE: This study sought to understand how the dietary source of carbohydrates, either high-fructose corn syrup (HFCS) or complex carbohydrates, affects energy expenditure (EE) measures, appetitive sensations, and hormones during 24 hours of overfeeding. METHODS: Seventeen healthy participants with normal glucose regulation had 24-hour EE measures and fasting blood and 24-hour urine collection during four different 1-day diets, including an energy-balanced diet, fasting, and two 75% carbohydrate diets (5% fat) given at 200% of energy requirements with either HFCS or whole-wheat foods as the carbohydrate source. In eight volunteers, hunger was assessed with visual analog scales the morning after the diets. RESULTS: Compared with energy balance, 24-hour EE increased 12.8% ± 6.9% with carbohydrate overfeeding (P < 0.0001). No differences in 24-hour EE or macronutrient utilization were observed between the two high-carbohydrate diets; however, sleeping metabolic rate was higher after the HFCS diet (Δ = 35 ± 48 kcal [146 ± 200 kJ]; P = 0.01). Insulin, ghrelin, and triglycerides increased the morning after both overfeeding diets. Urinary cortisol concentrations (82.8 ± 35.9 vs. 107.6 ± 46.9 nmol/24 h; P = 0.01) and morning-after hunger scores (Δ = 2.4 ± 2.0 cm; P = 0.01) were higher with HFCS overfeeding. CONCLUSIONS: The dietary carbohydrate source while overeating did not affect 24-hour EE, but HFCS overconsumption may predispose individuals to further overeating due to increased glucocorticoid release and increased hunger the following morning.

Energy Expenditure Responses to Fasting and Overfeeding Identify Phenotypes Associated With Weight Change.

Because it is unknown whether 24-h energy expenditure (EE) responses to dietary extremes will identify phenotypes associated with weight regulation, the aim of this study was to determine whether such responses to fasting or overfeeding are associated with future weight change. The 24-h EE during energy balance, fasting, and four different overfeeding diets with 200% energy requirements was measured in a metabolic chamber in 37 subjects with normal glucose regulation while they resided on our clinical research unit. Diets were given for 24 h each and included the following: (1) low protein (3%), (2) standard (50% carbohydrate, 20% protein), (3) high fat (60%), and (4) high carbohydrate (75%). Participants returned for follow-up 6 months after the initial measures. The decrease in 24-h EE during fasting and the increase with overfeeding were correlated. A larger reduction in EE during fasting, a smaller EE response to low-protein overfeeding, and a larger response to high-carbohydrate overfeeding all correlated with weight gain. The association of the fasting EE response with weight change was not independent from that of low protein in a multivariate model. We identified the following two independent propensities associated with weight gain: a predilection for conserving energy during caloric and protein deprivation and a profligate response to large amounts of carbohydrates.