Overweight and obese status is a major global public health issue associated with significantly increased morbidity and mortality risks [6]. In 2016, the World Health Organization (WHO) estimated that approximately 1.9 billion adults aged 18 years or older were overweight, while 650 million were obese. This 2016 report further reported 41 million children under the age of 5 years as overweight or obese, with over 340 million children and adolescents aged between 5 and 19 years as such [6-8]. In the context of the ongoing Covid-19 pandemic, it is well documented that obesity is associated with a strong increased mortality risk and negative disease prognosis [9-12] while tripling the risk of hospitalization [13,14].
Disease-related malnutrition is a serious challenge in treating patients with chronic or severe illness such as obesity; eventual hospitalization occurs at rates between 30-50% in populations of malnourished patients suffering from both hypo- and hypercaloric nutritional indispositions [6,7]. Many malnourished patients with attendant chronic illness are largely underserved by traditional channels of first-line healthcare professionals such as family doctors and general practitioners due to time-constraints, tendencies toward strict diagnostic care, and institutional inadequacies in addressing nutrition-based needs [15]. A gapbridging role in a patient’s nutrition-based healthcare is that of the community pharmacist, a provider considered to be one of the most accessible and trusted of all healthcare professionals [15]. The role of a community pharmacist not only includes the dispensation of prescription medicines and auxiliary medicinal products, but also the provision of services that adapt to their patients’ needs, such as nutritional counselling to improve quality of life outcomes [15]. According to the Royal Pharmaceutical Society of Great Britain, the holistic promotion of healthy lifestyles is listed as a primary goal of a pharmacy practice; this belies the traditional view that pharmacists operate strictly within the realm of drug dispensation and safe-use counsel [16].
Relatedly, Clinical Decision Support Systems (CDSS) are computerbased information and data aggregation systems designed to assist clinicians like community pharmacists in implementing clinical guidelines, evidence-based practices related to screening and other preventive services, clinical tests, and treatment at the point of care [17-19]. Patient information is entered manually or automatically through an Electronic Health Record (EHR) system, and CDSSs provide personalized patient assessments and treatment recommendations pursuant to the patient input data.
Manifold applications of CDSSs at varying levels of sophistication exist as an adjuvant for clinical care strategies. At the community pharmacy stratum, CDSSs represent propitious tools for effectively and efficiently improving patient outcomes with empirical and generalizable treatment recommendations. In Greece, community pharmacies are comprised of an interconnected and extensive communication network, an ideal setting for CDSS cross-adoption for health promotion and disease prevention.
During inchoate wave of the Covid-19 pandemic in Greece (between March and June of 2020), certified dietician and nutritionist services remained closed due to the then-urgent restrictive measures taken by the state in conjunction with the ad hoc scientific committee assembled for the management of the health crisis. Pharmacies, guided by the Panhellenic Pharmaceutical Association, remained open throughout and at the height of the pandemic. This unique circumstance accorded an auspicious opportunity to utilize a CDSS that provided nutritional assessment, screening, and a comprehensive dietary plan to evaluate its impact on patient BMI outcomes in the absence of confounding nutrition-based care access to patients. Our pharmacy practice located in Athens, Greece utilized a propriety food database CDSS to input patient data and to provide patients with an evidence-based sample diet according to CDSS output for the respective intervention arm. Our research objective was to evaluate the effectiveness of a CDSS on weight management as well as on weight-related goal setting and monitoring for a community pharmacy patient population.
Our study utilized a proprietary CDSS called Nutrient® and accompanying food database with the capability to provide a personalized nutritional and physical activity plan as well as lifestyle counseling. It was originally designed and developed by a multidisciplinary team of scientistspharmacists, dieticians, and marketing communication consultantsat the IASO Maternity Hospital for Obstetrics and Gynecology in Athens, Greece in 2018. The CDSS was first used to assist nutritional counseling to oncology patients with breast cancer on an outpatient basis with promising results [20].
In the present study, the neoteric CDSS was introduced to adult volunteers (males and females) in a community pharmacy situated in the southern suburbs of Athens, Greece from April of 2020 to June of 2020. The inclusion criteria were twofold: (i) study participants of ≥18 years of age, and (ii) completion of a 4-week follow up appointment in the pharmacy setting. The community pharmacist who administered the CDSS patient input data of anthropometric measurements, total daily energy expenditure, medical history, and drug treatment was previously trained in the use of CDSS.
The CDSS output provided the following services on individual basis: (1) a nutritional, medical, and physical screening as a generalized patient health assessment; (2) information on potential drug interactions; (3) a weight-goal monitoring and setting program; (4) a calculation of daily nutritional and caloric needs; and (5) the provision of a sample diet according to assigned intervention arm. Weight goals were automatically set pursuant to the CDSS proprietary software recommendations, and the enrolled participants were divided into three diet groups according to CDSS-recommended nutritional needs: DIET-A group, a hypocaloric diet for weight loss; DIET-B group, an isocaloric diet for maintenance of a healthy body weight; and DIET-C group, a hypercaloric diet for restoration of a healthy body weight.
At baseline, each enrolled participant completed an interview with the community pharmacist who recorded anthropometric measurements [body weight (kg), height (cm), and waist circumference (cm)], medical history, age (years), metabolic blood panel measurements at last check-up, physical activity, smoking status, and alcohol consumption patterns. Body weight was measured in the community pharmacy on a standing scale calibrated to 0.1kg of accuracy. Height was measured with a standard stadiometer to the nearest millimeter. Waist circumference was measured with a standard measuring tape to the nearest millimeter. All measurements were performed in duplicate by a single community pharmacist, and the average value was recorded. The community pharmacist also collected information on concurrent medication treatment and the use of nutritional supplements. These data were then registered in the CDSS.
Drug-Drug and Drug-Nutrient interactions were assessed by the CDSS. The pharmacist provided advice on non-pharmacological nutritional supplements to all participants according to individual need. Personalized advice was also administered on potential drug-herbal-nutrient interactions as necessary [21]. Participant Body Mass Index (BMI) was calculated by the CDSS, as the ratio of average weight (kg) to the square of average height (m2), and was subsequently taxonomized as underweight, normal weight, overweight or obese according to the “WHO, Global Database on Body Mass Index (BMI)” for adults [22]. The CDSS calculated daily nutritional requirements (kcal/day) based upon the recorded Total daily Energy Expenditure (TEE) after incorporating the physical activity factor [23,24] multiplied by the Resting Energy Expenditure (REE) [25]. To calculate REE, participants were provided an opportunity to obtain a recent (within 2 weeks of study participant uptake) measurement of indirect calorimetry [26] performed in a private or public clinic. For those participants who did not perform a recent indirect calorimetry measurement, the CDSS used a proprietary equation to estimate individuals’ REE [27-30].
The CDSS lifestyle output section disaggregated physical activity status into the following categories: Limitation of physical activities due to disability, Low Activity, Moderately Active, Active, and Vigorously Active. The CDSS output detailed the metes and bounds of each activity category so as to disambiguate self-reported status for study participants, with comprehensive descriptions of exercise type, time, and intensity as well as sedentary timescales to instantiate proper allocation into physical activity level categories.
Two additional imputation tools were incorporated to approximate participant nutritional status at baseline; (i) the Malnutrition Universal Screening Tool (MUST), a screening tool to identify adults, who are malnourished, at risk of malnutrition (undernutrition), or obese; and (ii) MNA®, a validated nutrition screening and assessment tool that can identify geriatric patients age 65 and above who are malnourished or at risk of malnutrition.
Participants assigned to DIET-A received a hypocaloric sample diet in which daily energy intake was less than TEE, at approximately net -500 kcal/day [31]. Participants assigned to DIET-B received an isocaloric balanced sample diet in which daily energy intake was equal with the TEE in order to maintain body weight. Participants assigned to DIET-C received a hypercaloric sample diet in which daily energy intake was increased by 250 kcal/day based upon participant-specific TEE in order to achieve the ideal body weight for height and age. In accordance with the Hippocratic imperatives of beneficence and autonomy for healthcare providers, participants’ health and weight-goal preferences were onboarded by the community pharmacist administrator in intervention placement. Thus, random intervention allocation was not feasible in the present study.
All sample diets were in line with Mediterranean dietary patterns [32-35]. The distribution of nutrients in relation to the total caloric value for each sample diet was as follows: 30% of total energy as fat (<10% as saturated fatty acids, 〜10% as monounsaturated fatty acids, and 〜10% as polyunsaturated fatty acids), 20% of total energy as protein, and 50% of total energy as carbohydrate. Each sample diet had approximately 300 milligram of dietary cholesterol per diem and 20-30 grams of fiber per diem. Macromolecule proportions remained commensurate between DIET-A, DIET-B, and DIET-C and only varied in total caloric value according to weightgoal intervention placement.
Endpoint body weight measurements (kg) of each enrolled participant was registered in the CDSS at a follow-up appointment after the 4-week intervention period. Weight was recorded in the community pharmacy setting on the same scale on which baseline were recorded, calibrated to .1 kg of accuracy. At follow-up, all participants adhering to their respective sample diet also completed a questionnaire assessing overall subjective results of the CDSS-based dietary recommendations. The structured questionnaire was developed by our research team based on a comprehensive literature review and extensive qualitative research as prior research on subject-perceived efficacy of CDSS-based dietary interventions is nonexistent to our current knowledge [36]. To ensure reliability in the developed questionnaire, we utilized items randomly selected from all factors in the validated USE (Usefulness, Satisfaction, Ease of use) questionnaire developed by Lund (2001) [37] and which was also adapted by Yu and Qian (2018) [38].
Patient Consent Statement
Adult volunteers who agreed to participate in the study were provided with a detailed information leaflet describing the aims, methods, benefits, and potential hazards of the study to establish participatory informed consent. In addition, each recruited participant provided a written informed consent agreement of which participants were instructed to retain a copy.
Finally, 22 participants (4 males and 18 females) completed the trial: N=13, DIET-A and N=9, DIET-B. Characteristics of the final sample size at baseline and follow up (4 weeks) are presented in (Table 2).
At the end of the 4-week intervention period, participants (N=22) completed the CDCC assessment questionnaire to evaluate the subject-perceived results of the intervention. To ensure questionnaire reliability, line items were randomly selected from all factors of the validated Usefulness, Satisfaction, and Ease of Use (USE) questionnaire developed by Lund (2001) for integration into the participant outcome survey [37]. The questionnaire was then pretested and subsequently retested to a sample group of 17 people, which demonstrated consistent survey results. All items were measured on a 7-point Likert scale anchored by 1- strongly disagree to 7 –strongly agree (Table 3).
Table 4 shows mean differences in body weight and BMI at baseline before the start of the trial and at the end of the intervention period (4 weeks). The Diet-A group registered a significant decrease in average body weight and BMI as compared to baseline by 2.68 kg (p=0.004) and by 1.11 kg/m2 (p=0.004), respectively. Participants in Diet-B registered no statistically significant change in average body weight (p=0.273) and BMI (p=0.320) at follow up.
|
N (%) |
Mean ± SD |
Minimum |
Maximum |
Males |
16 (28.1%) |
- |
- |
- |
Females |
41 (71.9%) |
- |
- |
- |
Age (years) |
57 |
51.0 ± 17.0 |
20.0 |
86.0 |
Body weight (kg) |
57 |
78.4 ± 21.2 |
47.6 |
183.0 |
Minimum IBW (kg) |
57 |
53.6 ± 10.4 |
33.9 |
78.6 |
Maximum IBW (kg) |
57 |
65.6 ± 12.5 |
46.4 |
96.0 |
Height (cm) |
57 |
165.8 ± 10.1 |
144.0 |
189.0 |
BMI (kg/m2) |
57 |
28.6 ± 7.4 |
17.5 |
54.6 |
BMI categories [N (%)] |
Underweight |
4 (7.0%) |
|
|
Normal weight |
21 (36.8%) |
|
|
|
Overweight |
20 (35.1%) |
|
|
|
Obese class I |
8 (14.0%) |
|
|
|
Obese class II |
1 (1.8%) |
|
|
|
Obese class III |
3 (5.3%) |
|
|
|
Intervention groups [N (%)] |
DIET-A |
41 (71.9%) |
|
|
DIET-B |
15 (26.3%) |
|
|
|
DIET-C |
1 (1.8%) |
|
|
|
Total daily energy needs (kcal/day) |
53 |
2645.0 ± 639.5 |
1454.2 |
3780.0 |
Total daily energy intake from sample diet (kcal/day) |
53 |
2234.6 ± 665.3 |
954.2 |
3330.0 |
Total daily energy intake from proteins (%) |
53 |
21.4 ± 2.6 |
17.0 |
23.0 |
Total daily energy intake from carbohydrates (%) |
53 |
44.4 ± 4.0 |
42.0 |
51.0 |
Total daily energy intake from fats (%) |
53 |
34.2 ± 1.3 |
32.0 |
35.0 |
Physical activity level categories [N (%)] |
Disability |
3 (6.0%) |
|
|
Low |
15 (30.0%) |
|
|
|
Moderate |
22 (44.0%) |
|
|
|
Active |
9 (18.0%) |
|
|
|
Vigorous |
1 (2.0%) |
|
|
|
Nutritional assessment [N (%)] |
At risk for Malnutrition |
8 (14.0%) |
|
|
At no risk - healthy eating |
35 (61.4%) |
|
|
|
Overnutrition |
14 (24.6%) |
|
|
|
N (%) |
Mean ± SD |
Minimum |
Maximum |
Males |
4 (18.2%) |
|
|
|
Females |
18 (81.8%) |
|
|
|
Age (years) |
22 |
59.7 ± 16.8 |
34.0 |
86.0 |
Body weight at baseline (kg) |
22 |
74.6 ± 15.8 |
49.0 |
120.0 |
Minimum IBW at baseline (kg) |
22 |
49.5 ± 7.9 |
33.9 |
67.1 |
Maximum IBW at baseline (kg) |
22 |
60.8 ± 9.2 |
47.5 |
82.0 |
Height (cm) |
22 |
161.8 ± 8.0 |
147.0 |
177.0 |
BMI at baseline (kg/m2) |
22 |
28.7 ± 6.5 |
|
|
BMI categories at baseline |
Underweight |
1 (4.5%) |
17.5 |
41.6 |
Normal weight |
|
|
|
|
Overweight |
7 (31.8%) |
|
|
|
Obese class I |
2 (9.1%) |
|
|
|
Obese class II |
1 (4.5%) |
|
|
|
Obese class III |
1 (4.5%) |
|
|
|
Intervention groups [N (%)] |
DIET-A |
13 (59.1%) |
|
|
DIET-B |
9 (40.9%) |
|
|
|
Total daily energy needs (kcal/day) |
20 |
2363.5 ± 667.0 |
1454.2 |
3780.0 |
Total daily energy intake from sample diet (kcal/day) |
20 |
2013.5 ± 740.9 |
954.2 |
3280.0 |
Total daily energy intake from proteins (%) |
20 |
20.6 ± 3.0 |
17.0 |
23.0 |
Total daily energy intake from carbohydrates (%) |
20 |
45.6 ± 4.5 |
42.0 |
51.0 |
Total daily energy intake from fats (%) |
20 |
33.8 ± 1.5 |
|
35.0 |
Physical activity level categories at baseline [N (%)] |
Disability |
2 (9.5%) |
|
|
Low |
4 (19.0%) |
|
|
|
Moderate |
10 (47.6%) |
|
|
|
Active |
4 (19.0%) |
|
|
|
Vigorous |
1 (4.8%) |
|
|
|
Nutritional Assessment |
At risk for Malnutrition |
4 (18.2%) |
|
|
At no risk – healthy eating |
15 (68.2%) |
|
|
|
Overnutrition |
3 (13.6%) |
32.0 |
|
|
Body weight at follow up (kg) |
22 |
72.8 ± 14.6 |
49.0 |
117.0 |
BMI at follow up (kg/m2) |
22 |
27.9 ± 5.8 |
17.5 |
39.0 |
BMI categories at follow up |
Underweight |
1 (4.5%) |
|
|
Normal weight |
10 (45.5%) |
|
|
|
Overweight |
8 (36.4%) |
|
|
|
Obese class I |
1 (4.5%) |
|
|
|
Obese class II |
2 (9.1%) |
|
|
|
Obese class III |
0 (0.0%) |
|
|
Question |
N |
Mean |
|
22 |
5.5 ± 1.4 |
|
22 |
5.0 ± 2.0 |
|
22 |
4.8 ± 1.8 |
|
22 |
4.6 ± 1.7 |
|
22 |
6.0 ± 1.4 |
|
22 |
4.8 ± 1.6 |
|
22 |
5.3 ± 1.6 |
Recently, Schüttler et al. (2017) found that electronic CDSS implementation for artificial nutrition management is beneficial for the critically ill and greatly reduces the likelihood of medication dosing errors [40]. Similarly, Nije et al. (2015) showed that electronic CDSSs are effective in improving clinician practices related to screening and other preventive care services, clinical tests, and treatments, especially in the assessment and management of cardiovascular disease and its associated risk factors in pharmacy [41]. In a 2014 pilot study conducted by the French National Nutrition and Health Program (PNNS), the implementation of a CDSS tool for dispensing nutrition advice to pharmacy patients proved beneficial and was positively reviewed by a cohort of surveyed patients [42]. A systematic literature review on the efficacy of computerized CDSSs on medicine prescribing outcomes by Robertson et al. (2010) showed significant salutary benefits of utilizing a pharmacist assisted CDSS to mitigate medicine safety issues [43]. To reify such benefits, Calloway et al. (2013) provided a case study on CDSS adoption for a large clinical pharmacy practice, finding that computerized CDSS implementation greatly augmented communication and knowledge among pharmacy staff and improved relationships with associated medical staff, nursing, and case management professionals [44].
A limited number of studies have focused on the contribution of community pharmacy interventions to weight management generally [45]. A recent systematic review evaluated ten studies on community pharmacybased weight management interventions, which were conducted in the United States of America (USA), United Kingdom (UK), Switzerland, Spain, and Denmark. The authors concluded that pharmacy-based weight management interventions can produce modest weight loss outcomes in overweight and obese populations [16]. In Australia particularly, the community pharmacy seems to be an ideal setting to aid in obesity management and prevention [15].
Plainly, socio-cultural mores, population health status, legacy healthcare infrastructure, and political climate all conspire inform community pharmacy-based intervention efficacy and its apposite application. In Greece, the community pharmacy holds a number of benefits as a setting for public health intervention. Greece contains the highest number pharmacies per capita in the European Union (97 per 100.000 inhabitants). Greek community pharmacies operate with extended opening hours and without requisite appointment scheduling for medication dispensation service and health consultation. As such, community pharmacy can be more accessible than other comparable healthcare service sectors.
To our knowledge, our pilot study is the first intervention to evaluate an advanced integration of a CDSS that utilizes structured data to forecast optimal nutritional support for patients within the community pharmacy setting. Not only did this intervention result in a significant and robust increase in the nutritional awareness of community pharmacy patients assigned to CDSS-based dietary interventions, but it also demonstrated the efficacy of specific CDSS-generated sample diets for weight loss and weight maintenance within a community pharmacy patient population in Athens, Greece. All adult participants achieved their intervention-directed primary goal of weight loss (DIET-A) or weight maintenance (DIET-B) after the 4week CDSS-assisted nutritional intervention. As operated by a community pharmacist, a CDSS-directed dietary program represents an effective and easy-to-use tool for obesity prevention and management for pharmacy patients and patrons.
Further, our interventional study included a thorough nutrition screening and assessment. Drug interaction and food-drug interaction assessments are vital components of the comprehensive implementation of a safe and effective dietary intervention. In a previous study, our research team evaluated the implementation of a Web-based approach to pharmaceutical care in Greece. We observed that a significant proportion of Greek pharmacists reported that the use of a Web-based drug-food interaction software enhanced their role as health consultants and helped them to improve the quality of services provided [21]. In the present study, 12 participants who were prescribed therapeutic drug medication and were concurrently supplementing with natural products were advised to stop supplementation due to drug interactions as assessed by the CDSS. A CDSS such as the one used in the present study incorporates a Web-based system for detecting potentially hazardous drug interactions in consilience with its patient-specific dietary recommendations, allowing the pharmacist to provide a more fulsome standard of care to the patient.
Study Limitations
Our study had several significant limitations. The initial sample size of 57 study participants is manifestly small, and participant gender parity was not achieved with 16 males and 41 females in the study population at trial outset. Furthermore, a comparatively high attrition rate resulted in only 22 adult participants (4 males and 18 females) completing the trial at the 4-week followup appointment, with 13 and 9 participants in the DIET-A and DIET-B cohorts, respectively. As 4week trial, our study was also not able to capture the efficacy of CDSS-based dietary recommendations for sustained weight management. Participant allocation into dietary intervention arms was not random but rather assigned by CDSS output according to net caloric energy requirements for weight management; participants in the DIET-A and DIET-B cohort may have possessed significant differences in propensity for weight loss and in non-dietary lifestyle patterns not captured by BMI outcome data. Moreover, sample diet adherence was not monitored over the 4week intervention period.
Our study design included the provision of a DIET-C cohort, which provided a hypercaloric sample diet for the purpose of weight gain, however only 1 study participant was assigned to this dietary intervention, and this participant subsequently discontinued study participation. Further exploration on the efficacy of CDSSrecommended hypercaloric dietary patterns is warranted. An additional DIET-D cohort that recommended no change in dietary patterns for the 4-week intervention period to control for potential situational confounders such as ubiquitously reduced access restaurant dining due to state-level lockdown restrictions would have been an ideal inclusion in the trial but was infeasible due to clinical equipoise considerations and financial constraints. Furthermore, it is well documented that BMI can be an imprecise proxy for salubriousness in certain populations [46]. Future study should take into account more comprehensive anthropometric measurement and metabolic blood panels at followup.
Finally, the intervention took place in a single community pharmacy setting. Geographic and population selection effects are relevant given that all study participants were prior community pharmacy patients. Our research team has forwarded a future proposal to conduct a similar intervention across several community pharmacy patient populations in Athens, Greece. Further exploration with larger study populations and more robust confounder controls is warranted.
- Francesco Sofi, Claudio Macchi, Rosanna Abbate, Gian Franco Gensini, Alessandro Casin. Mediterranean diet and health. Biofactors. 2013;39(4):335-342.doi: 10.1002/biof.1096
- Sobotka L, ForbesA. Basics in clinical nutrition. Galen. 2019.
- Jennifer Crowley, Lauren Ball, Gerrit Jan Hiddink. Nutrition in medical education: a systematic review. The Lancet Planetary Health, 3(9): e379-e389. Lancet Planet Health. 2019;3(9):e379-e389. doi: 10.1016/S2542-5196(19)30171-8
- Baute V, Sampath-Kumar R, Nelson S, Basil B. Nutrition education for the health-care provider improves patient outcomes. Glob Adv Health Med. 2018 Aug 24;7:2164956118795995. doi: 10.1177/2164956118795995. eCollection 2018
- Lacey K, Pritchett E. Nutrition Care Process and Model: ADA adopts road map to quality care and outcomes management. The 2008 Update. J Am Diet Assoc. 2003 Aug;103(8):1061-72. doi: 10.1016/s0002-8223(03)00971-4
- Levesque RJR. Obesity and overweight. World Heal Organ. 2018:2561-5.
- Um ISI, Armour C, Krass I, Gill T, Chaar BB. Managing obesity in pharmacy: The Australian experience. Pharm World Sci. 2010 Dec;32(6):711-20. doi: 10.1007/s11096-010-9426-5. Epub 2010 Aug 12
- Dobbs R, Sawers C, Thompson F, Manyika J, Woetzel J, ChildP, Spatharou A. Overcoming obesity: an initial economic analysis. McKinsey Global Institute. AIMS Agriculture and Food. 2014; 4(3): 731-755.
- Cai Q, Chen F, Wang T, Luo F, Liu X, Wu Q, He Q, Wang Z, Liu Y, Liu L, Chen J, Xu L. Obesity and COVID-19 Severity in a Designated Hospital in Shenzhen, China. Diabetes Care. 2020 Jul;43(7):1392-1398. doi: 10.2337/dc20-0576
- Dietz W, Santos-Burgoa C. Obesity and its Implications for COVID-19 Mortality. Obesity (Silver Spring). 2020 Jun;28(6):1005. doi: 10.1002/oby.22818
- Kalligeros M, Shehadeh F, Mylona EK, Benitez G, Beckwith CG, Chan PA, Mylonakis E. Association of Obesity with Disease Severity Among Patients with Coronavirus Disease 2019. Obesity (Silver Spring). 2020 Jul;28(7):1200-1204. doi: 10.1002/oby.22859
- Lighter J, Phillips M, Hochman S, Sterling S, Johnson D, Francois F, Stachel A. Obesity in Patients Younger Than 60 Years Is a Risk Factor for COVID-19 Hospital Admission. Clin Infect Dis. 2020 Jul 28;71(15):896-897. doi: 10.1093/cid/ciaa415
- Simonnet A, Chetboun M, Poissy J, Raverdy V, Noulette J, Duhamel A, Labreuche J, Mathieu D, Pattou F, Jourdain M; LICORN and the Lille COVID-19 and Obesity study group. High Prevalence of Obesity in Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Requiring Invasive Mechanical Ventilation. Obesity (Silver Spring). 2020 Jul;28(7):1195-1199. doi: 10.1002/oby.22831
- Petrilli CM, Jones SA, Yang J, Rajagopalan H, O'Donnell L, Chernyak Y, Tobin KA, Cerfolio RJ, Francois F, Horwitz LI. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ. 2020 May 22;369:m1966. doi: 10.1136/bmj.m1966
- Um IS, Armour C, Krass I, Gill T, Chaar BB. Weight management in community pharmacy: what do the experts think? Int J Clin Pharm. 2013 Jun;35(3):447-54. doi: 10.1007/s11096-013-9761-4
- Eades CE, Ferguson JS, O'Carroll RE. Public health in community pharmacy: a systematic review of pharmacist and consumer views. BMC Public Health. 2011 Jul 21;11:582. doi: 10.1186/1471-2458-11-582
- Curtain C, Peterson GM. Review of computerized clinical decision support in community pharmacy. J Clin Pharm Ther. 2014 Aug;39(4):343-8. doi: 10.1111/jcpt.12168
- Naureckas SM, Zweigoron R, Haverkamp KS, Kaleba EO, Pohl SJ, Ariza AJ. Developing an electronic clinical decision support system to promote guideline adherence for healthy weight management and cardiovascular risk reduction in children: a progress update. Transl Behav Med. 2011 Mar;1(1):103-7. doi: 10.1007/s13142-011-0019-1
- Paulsen MM, Hagen MLL, Frøyen MH, Foss-Pedersen RJ, Bergsager D, Tangvik RJ, Andersen LF. A Dietary Assessment App for Hospitalized Patients at Nutritional Risk: Development and Evaluation of the MyFood App. JMIR Mhealth Uhealth. 2018 Sep 7;6(9):e175. doi: 10.2196/mhealth.9953
- Skouroliakou M, Grosomanidis D, Massara P, Kostara C, Papandreou P, Ntountaniotis D, Xepapadakis G. Serum antioxidant capacity, biochemical profile and body composition of breast cancer survivors in a randomized Mediterranean dietary intervention study. Eur J Nutr. 2018 Sep;57(6):2133-2145. doi: 10.1007/s00394-017-1489-9
- Skouroliakou M, Thanopoulos MN, Maravelias G, Papandreou P, Ntountaniotis D, Daskalou E, Karagiozoglou-Lampoudi T. Nutrition-drug interactions: a Web-based approach to pharmaceutical care in Greece. J Am Pharm Assoc (2003). 2014 Jul-Aug;54(4):419-26. doi: 10.1331/JAPhA.2014.13194
- WHO | Global Database on Body Mass Index (BMI). Available at: https://www.who.int/nutrition/databases/bmi/en (last assessed 2019 April 26).
- Saris WH, Blair SN, van Baak MA, Eaton SB, Davies PS, Di Pietro L, Fogelholm M, Rissanen A, Schoeller D, Swinburn B, Tremblay A, Westerterp KR, Wyatt H. How much physical activity is enough to prevent unhealthy weight gain? Outcome of the IASO 1st Stock Conference and consensus statement. Obes Rev. 2003 May;4(2):101-14. doi: 10.1046/j.1467-789x.2003.00101.x
- Donnelly JE, Blair SN, Jakicic JM, Manore MM, Rankin JW, Smith BK. Appropriate physical activity intervention strategies for weight loss and prevention of weight regain for adults. Med Sci Sports Exerc. 2009;41:459-71
- James W, Schofield E. Human energy requirements: a manual for planners and nutritionists. Am J Clin Nutr. 1991;53:1506.
- Haugen HA, Chan LN, Li F. Invited review indirect calorimetry: a practical guide for clinicians. Nutr Clin Pract. 2007:377-88.
- Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990 Feb;51(2):241-7. doi: 10.1093/ajcn/51.2.241
- Harris JA, Benedict FG. A Biometric Study of Human Basal Metabolism. Proc Natl Acad Sci U S A. 1918 Dec;4(12):370-3. doi: 10.1073/pnas.4.12.370
- Scalfi L, Marra M, De Filippo E, Caso G, Pasanisi F, Contaldo F. The prediction of basal metabolic rate in female patients with anorexia nervosa. Int J Obes Relat Metab Disord. 2001 Mar;25(3):359-64. doi: 10.1038/sj.ijo.0801547
- Henry CJ. Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr. 2005 Oct;8(7A):1133-52. doi: 10.1079/phn2005801
- Ramage S, Farmer A, Eccles KA, McCargar L. Healthy strategies for successful weight loss and weight maintenance: a systematic review. Appl Physiol Nutr Metab. 2014 Jan;39(1):1-20. doi: 10.1139/apnm-2013-0026
- Estruch R, Ros E, Salas-Salvadó J, Covas MI, Corella D, Arós F, Gómez-Gracia E, Ruiz-Gutiérrez V, Fiol M, Lapetra J, Lamuela-Raventos RM, Serra-Majem L, Pintó X, Basora J, Muñoz MA, Sorlí JV, Martínez JA, Martínez-González MA; PREDIMED Study Investigators. Primary prevention of cardiovascular disease with a Mediterranean diet. N Engl J Med. 2013 Apr 4;368(14):1279-90. doi: 10.1056/NEJMoa1200303
- Rees K, Hartley L, Flowers N, Clarke A, Hooper L, Thorogood M, Stranges S. 'Mediterranean' dietary pattern for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev. 2013 Aug 12;(8):CD009825. doi: 10.1002/14651858.CD009825.pub2
- Korre M, Tsoukas MA, Frantzeskou E, Yang J, Kales SN. Mediterranean Diet and Workplace Health Promotion. Curr Cardiovasc Risk Rep. 2014;8(12):416. doi: 10.1007/s12170-014-0416-3
- Kopf JC, Suhr MJ, Clarke J, Eyun SI, Riethoven JM, Ramer-Tait AE, Rose DJ. Role of whole grains versus fruits and vegetables in reducing subclinical inflammation and promoting gastrointestinal health in individuals affected by overweight and obesity: a randomized controlled trial. Nutr J. 2018 Jul 30;17(1):72. doi: 10.1186/s12937-018-0381-7
- Stavrianea A, Kamenidou I. Generation z and religion in times of crisis. In: Strategic Innovative Marketing. Kavoura A, Sakas D, Tomaras P (eds) Strategic Innovative Marketing. Springer Proceedings in Business and Economics. Springer, Cham. 205-211. DOI: 10.1007/978-3-319-56288-9_28
- Lund AM. Measuring usability with the USE questionnaire. Usability Interface. 2001;8:3-6.
- Yu P, Qian S. Developing a theoretical model and questionnaire survey instrument to measure the success of electronic health records in residential aged care. PLoS One. 2018 Jan 9;13(1):e0190749. doi: 10.1371/journal.pone.0190749
- Fugh-Berman A, Ernst E. Herb-drug interactions: review and assessment of report reliability. Br J Clin Pharmacol. 2001 Nov;52(5):587-95. doi: 10.1046/j.0306-5251.2001.01469.x. Erratum in: Br J Clin Pharmacol 2002 Apr;53(4):449P
- Schüttler C, Hinderer M, Kraus S, Lang AK, Prokosch HU, Castellanos I. Requirements Analysis for a Clinical Decision Support System Aiming at Improving the Artificial Nutrition of Critically Ill Patients. Stud Health Technol Inform. 2017;243:137-141
- Njie GJ, Proia KK, Thota AB, Finnie RKC, Hopkins DP, Banks SM, Callahan DB, Pronk NP, Rask KJ, Lackland DT, Kottke TE; Community Preventive Services Task Force. Clinical Decision Support Systems and Prevention: A Community Guide Cardiovascular Disease Systematic Review. Am J Prev Med. 2015 Nov;49(5):784-795. doi: 10.1016/j.amepre.2015.04.006
- Charuel A, Prevost V. Conseils nutritionnels à l’officine dans le cadre du Programme National Nutrition Santé. Ann Pharm Fr. 2014;72:337-47
- Robertson J, Walkom E, Pearson SA, Hains I, Williamsone M, Newby D. The impact of pharmacy computerised clinical decision support on prescribing, clinical and patient outcomes: a systematic review of the literature. Int J Pharm Pract. 2010 Apr;18(2):69-87
- Calloway S, Akilo HA, Bierman K. Impact of a clinical decision support system on pharmacy clinical interventions, documentation efforts, and costs. Hosp Pharm. 2013 Oct;48(9):744-52. doi: 10.1310/hpj4809-744
- Weidmann AE, MacLure K, Marshall S, Gray G, Stewart D. Promoting weight management services in community pharmacy: perspectives of the pharmacy team in Scotland. Int J Clin Pharm. 2015 Aug;37(4):599-606. doi: 10.1007/s11096-015-0102-7
- Nuttall FQ. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr Today. 2015 May;50(3):117-128. doi: 10.1097/NT.0000000000000092