Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation.

BACKGROUND: Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications. OBJECTIVE: This study aims to establish a novel methodology for identifying individuals likely to exhibit DS, identify the most influential features in a more explainable way via probabilistic measures, and propose tools that can be used in real-world applications. METHODS: The study used 3 data sets: PROACTIVE, the Brazilian National Health Survey (Pesquisa Nacional de Saúde [PNS]) 2013, and PNS 2019, comprising sociodemographic and health-related features. A Bayesian network was used for feature selection. Selected features were then used to train machine learning models to predict DS, operationalized as a score of ≥10 on the 9-item Patient Health Questionnaire. The study also analyzed the impact of varying sensitivity rates on the reduction of screening interviews compared to a random approach. RESULTS: The methodology allows the users to make an informed trade-off among sensitivity, specificity, and a reduction in the number of interviews. At the thresholds of 0.444, 0.412, and 0.472, determined by maximizing the Youden index, the models achieved sensitivities of 0.717, 0.741, and 0.718, and specificities of 0.644, 0.737, and 0.766 for PROACTIVE, PNS 2013, and PNS 2019, respectively. The area under the receiver operating characteristic curve was 0.736, 0.801, and 0.809 for these 3 data sets, respectively. For the PROACTIVE data set, the most influential features identified were postural balance, shortness of breath, and how old people feel they are. In the PNS 2013 data set, the features were the ability to do usual activities, chest pain, sleep problems, and chronic back problems. The PNS 2019 data set shared 3 of the most influential features with the PNS 2013 data set. However, the difference was the replacement of chronic back problems with verbal abuse. It is important to note that the features contained in the PNS data sets differ from those found in the PROACTIVE data set. An empirical analysis demonstrated that using the proposed model led to a potential reduction in screening interviews of up to 52% while maintaining a sensitivity of 0.80. CONCLUSIONS: This study developed a novel methodology for identifying individuals with DS, demonstrating the utility of using Bayesian networks to identify the most significant features. Moreover, this approach has the potential to substantially reduce the number of screening interviews while maintaining high sensitivity, thereby facilitating improved early identification and intervention strategies for individuals experiencing DS.

Diet quality in cystic fibrosis - associations with patient reported outcome measures and enablers and barriers to eating a healthy diet: A protocol paper for a mixed methods study.

Background: People with cystic fibrosis (PWCF) have increased energy requirements. However, in recent years concerns have emerged regarding the 'cystic fibrosis (CF) diet' in terms of reliance on energy-dense, nutrient poor foods, which tend to be higher in saturated fat, sugar, and salt. These foods lack essential nutrients and are aetiologically linked with diet-related chronic diseases. The aim is to explore habitual dietary intakes in PWCF and (i) assess adherence to CF dietary guidelines and population specific healthy eating guidelines; (ii) derive a diet quality score and the inflammatory potential for the average diet consumed by PWCF and assess associations with patient reported outcome measures; (iii) assess drivers for current consumption patterns and enablers and barriers to eating a healthy diet. Methods: The aim is to recruit between 100-180 PWCF. A mixed methods study will be performed. Using three-day food diaries and food frequency questionnaires, aims (i) and (ii) will be addressed. The Dietary Approaches to Stop Hypertension (DASH) score and Healthy Eating Index-International (HEI-I) will derive diet quality scores. The Dietary Inflammatory Index (DII®) will ascertain inflammatory potential of the diet. Validated questionnaires will be used to report health related quality of life measures. Online focus groups and semi-structured interview with PWCF will address aim (iii). Conclusions: It is timely to revise dietary priorities and targets for CF. However, a greater understanding of what adults with CF currently consume and what they require in terms of nutrition and dietary guidance into the future is needed. In doing so, this research will help to clarify nutrition priorities and simplify the dietary aspects of CF treatment, thereby supporting adherence.

Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review.

BACKGROUND: Poor diet, alcohol use, and tobacco smoking have been identified as strong determinants of chronic diseases, such as cardiovascular disease, diabetes, and cancer. Smartphones have the potential to provide a real-time, pervasive, unobtrusive, and cost-effective way to measure these health behaviors and deliver instant feedback to users. Despite this, the validity of using smartphones to measure these behaviors is largely unknown. OBJECTIVE: The aim of our review is to identify existing smartphone-based approaches to measure these health behaviors and critically appraise the quality of their measurement properties. METHODS: We conducted a systematic search of the Ovid MEDLINE, Embase (Elsevier), Cochrane Library (Wiley), PsycINFO (EBSCOhost), CINAHL (EBSCOHost), Web of Science (Clarivate), SPORTDiscus (EBSCOhost), and IEEE Xplore Digital Library databases in March 2020. Articles that were written in English; reported measuring diet, alcohol use, or tobacco use via a smartphone; and reported on at least one measurement property (eg, validity, reliability, and responsiveness) were eligible. The methodological quality of the included studies was assessed using the Consensus-Based Standards for the Selection of Health Measurement Instruments Risk of Bias checklist. Outcomes were summarized in a narrative synthesis. This systematic review was registered with PROSPERO, identifier CRD42019122242. RESULTS: Of 12,261 records, 72 studies describing the measurement properties of smartphone-based approaches to measure diet (48/72, 67%), alcohol use (16/72, 22%), and tobacco use (8/72, 11%) were identified and included in this review. Across the health behaviors, 18 different measurement techniques were used in smartphones. The measurement properties most commonly examined were construct validity, measurement error, and criterion validity. The results varied by behavior and measurement approach, and the methodological quality of the studies varied widely. Most studies investigating the measurement of diet and alcohol received very good or adequate methodological quality ratings, that is, 73% (35/48) and 69% (11/16), respectively, whereas only 13% (1/8) investigating the measurement of tobacco use received a very good or adequate rating. CONCLUSIONS: This review is the first to provide evidence regarding the different types of smartphone-based approaches currently used to measure key behavioral risk factors for chronic diseases (diet, alcohol use, and tobacco use) and the quality of their measurement properties. A total of 19 measurement techniques were identified, most of which assessed dietary behaviors (48/72, 67%). Some evidence exists to support the reliability and validity of using smartphones to assess these behaviors; however, the results varied by behavior and measurement approach. The methodological quality of the included studies also varied. Overall, more high-quality studies validating smartphone-based approaches against criterion measures are needed. Further research investigating the use of smartphones to assess alcohol and tobacco use and objective measurement approaches is also needed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-https://doi.org/10.1186/s13643-020-01375-w.

Measurement properties of smartphone approaches to assess key lifestyle behaviours: protocol of a systematic review.

BACKGROUND: Six core behavioural risk factors (poor diet, physical activity, sedentary behaviour, alcohol misuse, smoking and unhealthy sleep patterns) have been identified as strong determinants of chronic disease, such as cardiovascular disease, diabetes and cancers. Smartphones have the potential to provide a real-time, pervasive, unobtrusive and cost-effective way to measure health behaviours and deliver instant feedback to users. Despite this, validity of using smartphones to measure these six key behaviours is largely unknown. The proposed systematic review aims to address this gap by identifying existing smartphone-based approaches to measure these health behaviours and critically appraising, comparing and summarizing the quality of their measurement properties. METHODS: A systematic search of the Ovid MEDLINE, Embase (Elsevier), Cochrane Library (Wiley), PsychINFO (EBSCOhost), CINAHL (EBSCOHost), Web of Science (Clarivate), SPORTDiscus (EBSCOhost) and IEEE Xplore Digital Library databases will be conducted from January 2007 to March 2020. Eligible studies will be those written in English that measure at least one of the six health behaviours of interest via a smartphone and report on at least one measurement property. The primary outcomes will be validity, reliability and/or responsiveness of these measurement approaches. A secondary outcome will be the feasibility (e.g. user burden, usability and cost) of identified approaches. No restrictions will be placed on the participant population or study design. Two reviewers will independently screen studies for eligibility, extract data and assess the risk of bias. The study methodological quality (or bias) will be appraised using an appropriate tool. Our results will be described in a narrative synthesis. If feasible, random effects meta-analysis will be conducted where appropriate. DISCUSSION: The results from this review will provide important information about the types of smartphone-based approaches currently available to measure the core behavioural risk factors for chronic disease and the quality of their measurement properties. It will allow recommendations on the most suitable and effective measures of these lifestyle behaviours using smartphones. Valid and reliable measurement of these behaviours and risk factor opens the door to targeted and real-time delivery of health behaviour interventions, providing unprecedented opportunities to offset the trajectory toward chronic disease. SYSTEMATIC REVIEW REGISTRATION: PROSPERO: CRD42019122242.