RESUMO
BACKGROUND: Heterogeneity of reported outcomes can impact the certainty of evidence for prehabilitation. The objective of this scoping review was to systematically map outcomes and assessment tools used in trials of surgical prehabilitation. METHODS: MEDLINE, EMBASE, PsychInfo, Web of Science, CINAHL, and Cochrane were searched in February 2023. Randomised controlled trials of unimodal or multimodal prehabilitation interventions (nutrition, exercise, psychological support) lasting at least 7 days in adults undergoing elective surgery were included. Reported outcomes were classified according to the International Society for Pharmacoeconomics and Outcomes Research framework. RESULTS: We included 76 trials, mostly focused on abdominal or orthopaedic surgeries. A total of 50 different outcomes were identified, measured using 184 outcome assessment tools. Observer-reported outcomes were collected in 86% of trials (n=65), with hospital length of stay being most common. Performance outcomes were reported in 80% of trials (n=61), most commonly as exercise capacity assessed by cardiopulmonary exercise testing. Clinician-reported outcomes were included in 78% (n=59) of trials and most frequently included postoperative complications with Clavien-Dindo classification. Patient-reported outcomes were reported in 76% (n=58) of trials, with health-related quality of life using the 36- or 12-Item Short Form Survey being most prevalent. Biomarker outcomes were reported in 16% of trials (n=12) most commonly using inflammatory markers assessed with C-reactive protein. CONCLUSIONS: There is substantial heterogeneity in the reporting of outcomes and assessment tools across surgical prehabilitation trials. Identification of meaningful outcomes, and agreement on appropriate assessment tools, could inform the development of a prehabilitation core outcomes set to harmonise outcome reporting and facilitate meta-analyses.
Assuntos
Exercício Pré-Operatório , Ensaios Clínicos Controlados Aleatórios como Assunto , Humanos , Complicações Pós-Operatórias/prevenção & controle , Medidas de Resultados Relatados pelo Paciente , Cuidados Pré-Operatórios/métodos , Avaliação de Resultados em Cuidados de SaúdeRESUMO
BACKGROUND: There is no universally accepted definition for surgical prehabilitation. The objectives of this scoping review were to (1) identify how surgical prehabilitation is defined across randomised controlled trials and (2) propose a common definition. METHODS: The final search was conducted in February 2023 using MEDLINE, Embase, PsycINFO, Web of Science, CINAHL, and Cochrane. We included randomised controlled trials (RCTs) of unimodal or multimodal prehabilitation interventions (nutrition, exercise, and psychological support) lasting at least 7 days in adults undergoing elective surgery. Qualitative data were analysed using summative content analysis. RESULTS: We identified 76 prehabilitation trials of patients undergoing abdominal (n=26, 34%), orthopaedic (n=20, 26%), thoracic (n=14, 18%), cardiac (n=7, 9%), spinal (n=4, 5%), and other (n=5, 7%) surgeries. Surgical prehabilitation was explicitly defined in more than half of these RCTs (n=42, 55%). Our findings consolidated the following definition: 'Prehabilitation is a process from diagnosis to surgery, consisting of one or more preoperative interventions of exercise, nutrition, psychological strategies and respiratory training, that aims to enhance functional capacity and physiological reserve to allow patients to withstand surgical stressors, improve postoperative outcomes, and facilitate recovery.' CONCLUSIONS: A common definition is the first step towards standardisation, which is needed to guide future high-quality research and advance the field of prehabilitation. The proposed definition should be further evaluated by international stakeholders to ensure that it is comprehensive and globally accepted.
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Exercício Pré-Operatório , Ensaios Clínicos Controlados Aleatórios como Assunto , Humanos , Cuidados Pré-Operatórios/métodos , Terminologia como AssuntoRESUMO
BACKGROUND: Thorough dietary assessment is essential to obtain accurate food and nutrient intake data yet challenging because of the limitations of current methods. Image-based methods may decrease energy underreporting and increase the validity of self-reported dietary intake. Keenoa is an image-assisted food diary that integrates artificial intelligence food recognition. We hypothesized that Keenoa is as valid for dietary assessment as the automated self-administered 24-hour recall (ASA24)-Canada and better appreciated by users. OBJECTIVE: We aimed to evaluate the relative validity of Keenoa against a 24-hour validated web-based food recall platform (ASA24) in both healthy individuals and those living with diabetes. Secondary objectives were to compare the proportion of under- and overreporters between tools and to assess the user's appreciation of the tools. METHODS: We used a randomized crossover design, and participants completed 4 days of Keenoa food tracking and 4 days of ASA24 food recalls. The System Usability Scale was used to assess perceived ease of use. Differences in reported intakes were analyzed using 2-tailed paired t tests or Wilcoxon signed-rank test and deattenuated correlations by Spearman coefficient. Agreement and bias were determined using the Bland-Altman test. Weighted Cohen κ was used for cross-classification analysis. Energy underreporting was defined as a ratio of reported energy intake to estimated resting energy expenditure <0.9. RESULTS: A total of 136 participants were included (mean 46.1, SD 14.6 years; 49/136, 36% men; 31/136, 22.8% with diabetes). The average reported energy intakes (kcal/d) were 2171 (SD 553) in men with Keenoa and 2118 (SD 566) in men with ASA24 (P=.38) and, in women, 1804 (SD 404) with Keenoa and 1784 (SD 389) with ASA24 (P=.61). The overall mean difference (kcal/d) was -32 (95% CI -97 to 33), with limits of agreement of -789 to 725, indicating acceptable agreement between tools without bias. Mean reported macronutrient, calcium, potassium, and folate intakes did not significantly differ between tools. Reported fiber and iron intakes were higher, and sodium intake lower, with Keenoa than ASA24. Intakes in all macronutrients (r=0.48-0.73) and micronutrients analyzed (r=0.40-0.74) were correlated (all P<.05) between tools. Weighted Cohen κ scores ranged from 0.30 to 0.52 (all P<.001). The underreporting rate was 8.8% (12/136) with both tools. Mean System Usability Scale scores were higher for Keenoa than ASA24 (77/100, 77% vs 53/100, 53%; P<.001); 74.8% (101/135) of participants preferred Keenoa. CONCLUSIONS: The Keenoa app showed moderate to strong relative validity against ASA24 for energy, macronutrient, and most micronutrient intakes analyzed in healthy adults and those with diabetes. Keenoa is a new, alternative tool that may facilitate the work of dietitians and nutrition researchers. The perceived ease of use may improve food-tracking adherence over longer periods.
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Inteligência Artificial , Aplicativos Móveis , Adulto , Masculino , Feminino , Humanos , Estudos Cross-Over , Avaliação Nutricional , Fibras na DietaRESUMO
BACKGROUND: The COVID-19 pandemic and related lockdowns have impacted lifestyle behaviors, including eating habits and physical activity; yet, few studies have identified the emerging patterns of such changes and associated risk factors. OBJECTIVE: This study aims to identify the patterns of weight and lifestyle behavior changes, and the potential risk factors, resulting from the pandemic in Canadian adults. METHODS: Analyses were conducted on 1609 adults (18-89 years old; n=1450, 90.1%, women; n=1316, 81.8%, White) of the Canadian COVIDiet study baseline data (May-December 2020). Self-reported current and prepandemic weight, physical activity, smoking status, perceived eating habits, alcohol intake, and sleep quality were collected through online questionnaires. Based on these 6 indicator variables, latent class analysis (LCA) was used to identify lifestyle behavior change patterns. Associations with potential risk factors, including age, gender, ethnicity, education, income, chronic diseases, body image perception, and changes in the stress level, living situation, and work arrangement, were examined with logistic regressions. RESULTS: Participants' mean BMI was 26.1 (SD 6.3) kg/m2. Of the 1609 participants, 980 (60.9%) had a bachelor's degree or above. Since the pandemic, 563 (35%) had decreased income and 788 (49%) changed their work arrangement. Most participants reported unchanged weight, sleep quality, physical activity level, and smoking and alcohol consumption, yet 708 (44%) reported a perceived decrease in eating habit quality. From LCA, 2 classes of lifestyle behavior change emerged: healthy and less healthy (probability: 0.605 and 0.395, respectively; Bayesian information criterion [BIC]=15574, entropy=4.8). The healthy lifestyle behavior change group more frequently reported unchanged weight, sleep quality, smoking and alcohol intake, unchanged/improved eating habits, and increased physical activity. The less healthy lifestyle behavior change group reported significant weight gain, deteriorated eating habits and sleep quality, unchanged/increased alcohol intake and smoking, and decreased physical activity. Among risk factors, body image dissatisfaction (odds ratio [OR] 8.8, 95% CI 5.3-14.7), depression (OR 1.8, 95% CI 1.3-2.5), increased stress level (OR 3.4, 95% CI 2.0-5.8), and gender minority identity (OR 5.5, 95% CI 1.3-22.3) were associated with adopting less healthy behaviors in adjusted models. CONCLUSIONS: The COVID-19 pandemic has appeared to have influenced lifestyle behaviors unfavorably in some but favorably in others. Body image perception, change in stress level, and gender identity are factors associated with behavior change patterns; whether these will sustain over time remains to be studied. Findings provide insights into developing strategies for supporting adults with poorer mental well-being in the postpandemic context and promoting healthful behaviors during future disease outbreaks. TRIAL REGISTRATION: ClinicalTrials.gov NCT04407533; https://clinicaltrials.gov/ct2/show/NCT04407533.