Formative Evaluation of a Smartphone App for Monitoring Daily Meal Distribution and Food Selection in Adolescents: Acceptability and Usability Study.

BACKGROUND: Obesity interventions face the problem of weight regain after treatment as a result of low compliance. Mobile health (mHealth) technologies could potentially increase compliance and aid both health care providers and patients. OBJECTIVE: This study aimed to evaluate the acceptability and usability and define system constraints of an mHealth system used to monitor dietary habits of adolescents in real life, as a first step in the development of a self-monitoring and lifestyle management system against adolescent obesity. METHODS: We recruited 26 students from a high school in Stockholm, Sweden. After a 30-minute information meeting and 5-minute individual instruction on how to use an mHealth system (smartphone with app and two external sensors), participants used it for 2-3 weeks to objectively collect dietary habits. The app and sensors were used by the participants, without supervision, to record as many main meals and snacks as possible in real life. Feasibility was assessed following the "mHealth evidence reporting and assessment checklist," and usability was assessed by questionnaires. Compliance was estimated based on system use, where a registration frequency of 3 main meals (breakfast, lunch, and dinner) per day for the period of the experiment, constituted 100% compliance. RESULTS: Participants included in the analysis had a mean age of 16.8 years (SD 0.7 years) and BMI of 21.9 kg/m2 (SD 4.1 kg/m2). Due to deviations from study instructions, 2 participants were excluded from the analysis. During the study, 6 participants required additional information on system use. The system received a 'Good' grade (77.1 of 100 points) on the System Usability Scale, with most participants reporting that they were comfortable using the smartphone app. Participants expressed a willingness to use the app mostly at home, but also at school; most of their improvement suggestions concerned design choices for the app. Of all main meals, the registration frequency increased from 70% the first week to 76% the second week. Participants reported that 40% of the registered meals were home-prepared, while 34% of the reported drinks contained sugar. On average, breakfasts took place at 8:30 AM (from 5:00 AM to 2:00 PM), lunches took place at 12:15 PM (from 10:15 AM to 6:15 PM), and dinners took place at 7:30 PM (from 3:00 PM to 11:45 PM). When comparing meal occurrence during weekdays vs weekends, breakfasts and lunches were eaten 3 hours later during weekends, while dinner timing was unaffected. CONCLUSIONS: From an infrastructural and functional perspective, system use was feasible in the current context. The smartphone app appears to have high acceptability and usability in high school students, which are the intended end-users. The system appears promising as a relatively low-effort method to provide real-life dietary habit measurements associated with overweight and obesity risk.

"OPTImAL": an ontology for patient adherence modeling in physical activity domain.

BACKGROUND: Maintaining physical fitness is a crucial component of the therapeutic process for patients with cardiovascular disease (CVD). Despite the known importance of being physically active, patient adherence to exercise, both in daily life and during cardiac rehabilitation (CR), is low. Patient adherence is frequently composed of numerous determinants associated with different patient aspects (e.g., psychological, clinical, etc.). Understanding the influence of such determinants is a central component of developing personalized interventions to improve or maintain patient adherence. Medical research produced evidence regarding factors affecting patients' adherence to physical activity regimen. However, the heterogeneity of the available data is a significant challenge for knowledge reusability. Ontologies constitute one of the methods applied for efficient knowledge sharing and reuse. In this paper, we are proposing an ontology called OPTImAL, focusing on CVD patient adherence to physical activity and exercise training. METHODS: OPTImAL was developed following the Ontology Development 101 methodology and refined based on the NeOn framework. First, we defined the ontology specification (i.e., purpose, scope, target users, etc.). Then, we elicited domain knowledge based on the published studies. Further, the model was conceptualized, formalized and implemented, while the developed ontology was validated for its consistency. An independent cardiologist and three CR trainers evaluated the ontology for its appropriateness and usefulness. RESULTS: We developed a formal model that includes 142 classes, ten object properties, and 371 individuals, that describes the relations of different factors of CVD patient profile to adherence and adherence quality, as well as the associated types and dimensions of physical activity and exercise. 2637 logical axioms were constructed to comprise the overall concepts that the ontology defines. The ontology was successfully validated for its consistency and preliminary evaluated for its appropriateness and usefulness in medical practice. CONCLUSIONS: OPTImAL describes relations of 320 factors originated from 60 multidimensional aspects (e.g., social, clinical, psychological, etc.) affecting CVD patient adherence to physical activity and exercise. The formal model is evidence-based and can serve as a knowledge tool in the practice of cardiac rehabilitation experts, supporting the process of activity regimen recommendation for better patient adherence.

Predicting Real-Life Eating Behaviours Using Single School Lunches in Adolescents.

Large portion sizes and a high eating rate are associated with high energy intake and obesity. Most individuals maintain their food intake weight (g) and eating rate (g/min) rank in relation to their peers, despite food and environmental manipulations. Single meal measures may enable identification of "large portion eaters" and "fast eaters," finding individuals at risk of developing obesity. The aim of this study was to predict real-life food intake weight and eating rate based on one school lunch. Twenty-four high-school students with a mean (±SD) age of 16.8 yr (±0.7) and body mass index of 21.9 (±4.1) were recruited, using no exclusion criteria. Food intake weight and eating rate was first self-rated ("Less," "Average" or "More than peers"), then objectively recorded during one school lunch (absolute weight of consumed food in grams). Afterwards, subjects recorded as many main meals (breakfasts, lunches and dinners) as possible in real-life for a period of at least two weeks, using a Bluetooth connected weight scale and a smartphone application. On average participants recorded 18.9 (7.3) meals during the study. Real-life food intake weight was 327.4 g (±110.6), which was significantly lower (p = 0.027) than the single school lunch, at 367.4 g (±167.2). When the intra-class correlation of food weight intake between the objectively recorded real-life and school lunch meals was compared, the correlation was excellent (R = 0.91). Real-life eating rate was 33.5 g/min (±14.8), which was significantly higher (p = 0.010) than the single school lunch, at 27.7 g/min (±13.3). The intra-class correlation of the recorded eating rate between real-life and school lunch meals was very large (R = 0.74). The participants' recorded food intake weights and eating rates were divided into terciles and compared between school lunches and real-life, with moderate or higher agreement (κ = 0.75 and κ = 0.54, respectively). In contrast, almost no agreement was observed between self-rated and real-life recorded rankings of food intake weight and eating rate (κ = 0.09 and κ = 0.08, respectively). The current study provides evidence that both food intake weight and eating rates per meal vary considerably in real-life per individual. However, based on these behaviours, most students can be correctly classified in regard to their peers based on single school lunches. In contrast, self-reported food intake weight and eating rate are poor predictors of real-life measures. Finally, based on the recorded individual variability of real-life food intake weight and eating rate, it is not advised to rank individuals based on single recordings collected in real-life settings.

Cigarette Smoking Promotes Infection of Cervical Cells by High-Risk Human Papillomaviruses, but not Subsequent E7 Oncoprotein Expression.

Persistent cervical infection with high-risk human papillomaviruses (hrHPVs) is a necessary, but not sufficient, condition for the development of cervical cancer. Therefore, there are other co-factors facilitating the hrHPV carcinogenic process, one of which is smoking. To assess the effect of smoking on high-risk (hr) HPV DNA positivity and on the expression of HPV E7 oncoprotein, as a surrogate of persistent hrHPV infection, we used data from women recruited for the PIPAVIR project, which examined the role of E7 protein detection in cervical cancer screening. Women were tested for hrHPV DNA, using Multiplex Genotyping (MPG), and E7 protein, using a novel sandwich ELISA method, and gave information on their smoking habits. Among 1473 women, hrHPV prevalence was 19.1%. The odds ratio (OR) for hrHPV positivity of smokers compared to non-smokers was 1.785 (95% confidence intervals (CI): 1.365-2.332, p < 0.001). The ORs for E7 positivity, concerning hrHPV positive women, ranged from 0.720 to 1.360 depending on the E7 detection assay used, but this was not statistically significant. Smoking increases the probability of hrHPV infection, and smoking intensity is positively associated to this increase. Smoking is not related to an increased probability of E7 protein positivity for hrHPV positive women.

Human papillomavirus E7 protein detection as a method of triage to colposcopy of HPV positive women, in comparison to genotyping and cytology. Final results of the PIPAVIR study.

The objective of the presented cross-sectional-evaluation-screening study is the clinical evaluation of high-risk(hr)HPVE7-protein detection as a triage method to colposcopy for hrHPV-positive women, using a newly developed sandwich-ELISA-assay. Between 2013-2015, 2424 women, 30-60 years old, were recruited at the Hippokratio Hospital, Thessaloniki/Greece and the Im Mare Klinikum, Kiel/Germany, and provided a cervical sample used for Liquid Based Cytology, HPV DNA genotyping, and E7 detection using five different E7-assays: "recomWell HPV16/18/45KJhigh", "recomWell HPV16/18/45KJlow", "recomWell HPV39/51/56/59", "recomWell HPV16/31/33/35/52/58" and "recomWell HPVHRscreen" (for 16,18,31,33,35,39,45,51,52,56,58,59 E7), corresponding to different combinations of hrHPVE7-proteins. Among 1473 women with eligible samples, those positive for cytology (ASCUS+ 7.2%), and/or hrHPV DNA (19.1%) were referred for colposcopy. Cervical Intraepithelial Neoplasia grade 2 or worse (CIN2+) was detected in 27 women (1.8%). For HPV16/18-positive women with no triage, sensitivity, positive predictive value (PPV) and the number of colposcopies needed to detect one case of CIN2+ were 100.0%, 11.11% and 9.0 respectively. The respective values for E7-testing as a triage method to colposcopy ranged from 75.0-100.0%, 16.86-26.08% and 3.83-5.93. Sensitivity and PPV for cytology as triage for hrHPV(non16/18)-positive women were 45.45% and 27.77%; for E7 test the respective values ranged from 72.72-100.0% and 16.32-25.0%. Triage of HPV 16/18-positive women to colposcopy with the E7 test presents better performance than no triage, decreasing the number of colposcopies needed to detect one CIN2+. In addition, triage of hrHPV(non16/18)-positive women with E7 test presents better sensitivity and slightly worse PPV than cytology, a fact that advocates for a full molecular screening approach.

Diagnostic accuracy of high-risk HPV DNA genotyping for primary cervical cancer screening and triage of HPV-positive women, compared to cytology: preliminary results of the PIPAVIR study.

PURPOSE: The purpose of the presented PIPAVIR (persistent infections with human papillomaviruses; http://www.pipavir.com ) subanalysis is to assess the performance of high-risk (hr) HPV-DNA genotyping as a method of primary cervical cancer screening and triage of HPV positive women to colposcopy compared to liquid-based cytology (LBC) in an urban female population. METHODS: Women, aged 30-60, provided cervicovaginal samples at the Family-Planning Centre, Hippokratio Hospital of Thessaloniki, Greece, and the Department of Gynecology and Obstetrics in Mare Klinikum, Kiel, Germany. Cytology and HPV genotyping was performed using LBC and HPV Multiplex Genotyping (MPG), respectively. Women positive for cytology [atypical squamous cells of undetermined significance (ASC-US) or worse] or hrHPV were referred for colposcopy. RESULTS: Among 1723/1762 women included in the final analysis, hrHPV and HPV16/18 prevalence was 17.7 and 9.6%, respectively. Cytology was ASCUS or worse in 7.6%. Cervical Intraepithelial Neoplasia grade 2 or worse (CIN2+) was detected in 28 women (1.6%). Sensitivity of cytology (ASCUS or worse) and HPV DNA testing for the detection of CIN2+ was 50.0 and 100%, and specificity was 94.49 and 85.49%, respectively. The screening approach according to which only women positive for HPV16/18 and for hrHPV(non16/18) with ASCUS or worse were referred to colposcopy presented 78.57% sensitivity and 13.17% positive predictive value (PPV). CONCLUSIONS: HPV testing represents a more sensitive methodology for primary cervical cancer screening compared to cytology. For triage of HPV positive women to colposcopy, partial HPV genotyping offers better sensitivity than cytology, at the cost of higher number of colposcopies.

The effect of cigarette smoking on fetal heart rate tracing during pregnancy.

OBJECTIVE: The objective of this study is to investigate the alterations caused by smoking on the features of fetal heart rate (FHR) tracings as well as to make a comparison between pregnant smokers and pregnant women with intrauterine growth restriction (IUGR). STUDY DESIGN: A number of established features derived from linear and nonlinear fields were employed to study the possible influence of maternal smoking on FHR tracings. Moreover, correlation and measures of complexity of the FHR were explored, in order to get closer to the core of information that the signal of FHR tracings conveys. Data included FHR tracings from 61 uncomplicated singleton pregnancies, 16 pregnant smoker cases, and 15 pregnancies of women with IUGR. RESULTS: The analysis of FHR indicated that some parameters, such as mutual information (P=0.0025), multiscale entropy (P=0.01), and algorithmic complexity (P=0.024) appeared decreased in the group of pregnant smokers, while kurtosis (P=0.0011) increased. The comparison between pregnant smokers and pregnant women with IUGR indicated a reduction in Hjorth complexity (P=0.039) for the former. CONCLUSION: Smoking during pregnancy seems to induce differences in several linear and nonlinear indices in recordings of FHR tracings. This may be the consequence of an altered neurodevelopmental maturation possibly resulting from chronic fetal hypoxemia in cigarette-exposed fetuses.

Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals.

INTRODUCTION: Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder. MATERIALS AND METHODS: Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI. RESULTS: A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA. DISCUSSION: We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome. CONCLUSIONS: Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology. TRIAL REGISTRATION: ClinicalTrials.gov NCT01161381.

QRS analysis using wavelet transformation for the prediction of response to cardiac resynchronization therapy: a prospective pilot study.

BACKGROUND: Wider QRS and left bundle branch block morphology are related to response to cardiac resynchronization therapy (CRT). A novel time-frequency analysis of the QRS complex may provide additional information in predicting response to CRT. METHODS: Signal-averaged electrocardiograms were prospectively recorded, before CRT, in orthogonal leads and QRS decomposition in three frequency bands was performed using the Morlet wavelet transformation. RESULTS: Thirty eight patients (age 65±10years, 31 males) were studied. CRT responders (n=28) had wider baseline QRS compared to non-responders and lower QRS energies in all frequency bands. The combination of QRS duration and mean energy in the high frequency band had the best predicting ability (AUC 0.833, 95%CI 0.705-0.962, p=0.002) followed by the maximum energy in the high frequency band (AUC 0.811, 95%CI 0.663-0.960, p=0.004). CONCLUSIONS: Wavelet transformation of the QRS complex is useful in predicting response to CRT.

Screening of patients with Obstructive Sleep Apnea Syndrome using C4.5 algorithm based on non linear analysis of respiratory signals during sleep.

AIM: To classify patients with possible diagnosis of Obstructive Sleep Apnea Syndrome (OSAS) into groups according to the severity of the disease using a decision tree producing algorithm based on nonlinear analysis of 3 respiratory signals instead of the use of full polysomnography. PATIENTS-METHODS: Eighty-six consecutive patients referred to the Sleep Unit of a Pulmonology Department underwent full polysomnography and their tests were manually scored. Three nonlinear indices (Largest Lyapunov Exponent-LLE, Detrended Fluctuation Analysis-DFA and Approximate Entropy-APEN) were extracted from two respiratory signals (nasal cannula flow-F and thoracic belt-T). The oxygen saturation signal (SpO(2)) was also selected. The above measurements provided data to the C4.5 algorithm using a data mining application. RESULTS: Two decision trees were produced using linear and nonlinear data from 3 respiratory signals. The discrimination between normal subjects and sufferers from OSAS presented an accuracy of 84.9% and a recall of 90.3% using the variables age, sex, DFA from F and Time with SpO(2)<90% (T90). The classification of patients into severity groups had an accuracy of 74.2% and a recall of 81.1% using the variables APEN from F, DFA from F and T90. CONCLUSION: It is possible to have reliable predictions of the severity of OSAS using linear and nonlinear indices from only two respiratory signals during sleep instead of performing full polysomnography. The proposed algorithm could be used for screening patients suspected to suffer from OSAS.

The use of Data Mining in the categorization of patients with Azoospermia.

OBJECTIVE: Data Mining is a relatively new field of Medical Informatics. The aim of this study was to compare Data Mining diagnosis with clinical diagnosis by applying a Data Miner (DM) to a clinical dataset of infertile men with azoospermia. DESIGN: One hundred and forty-seven azoospermic men were clinically classified into four groups: a) obstructive azoospermia (n=63), b) non-obstructive azoospermia (n=71), c) hypergonadotropic hypogonadism (n=2), and d) hypogonadotropic hypogonadism (n=11). The DM (IBM's DB2/Intelligent Miner for Data 6.1) was asked to reproduce a four-cluster model. RESULTS: DM formed four groups of patients: a) eugonadal men with normal testicular volume and normal FSH levels (n=86), b) eugonadal men with significantly reduced testicular volume (median 6.5 cm3) and very high FSH levels (n=29), c) eugonadal men with moderately reduced testicular volume (median 14.5 cm3) and raised FSH levels (n=20), and d) hypogonadal men (n=12). Overall DM concordance rate in hypogonadal men was 92%, in obstructive azoospermia 73%, and in non-obstructive azoospermia 69%. CONCLUSIONS: Data Mining produces clinically meaningful results but different from those of the clinical diagnosis. It is possible that the use of large sets of structured and formalised data and continuous evaluation of DM results will generate a useful methodology for the Clinician.

Can P wave wavelet analysis predict atrial fibrillation after coronary artery bypass grafting?

The purpose of this study was the evaluation of Morlet wavelet analysis of the P wave as a means of predicting the development of atrial fibrillation (AF) in patients who undergo coronary artery bypass grafting (CABG). The P wave was analyzed using the Morlet wavelet in 50 patients who underwent successful CABG. Group A consisted of 17 patients, 12 men and 5 women, of mean age 66.9 +/- 5.9 years, who developed AF postoperatively. Group B consisted of 33 patients, 29 men and 4 women, mean age 62.4 +/- 7.8 years, who remained arrhythmid-free. Using custom-designed software, P wave duration and wavelet parameters expressing the mean and maximum energy of the P wave were calculated from 3-channel digital recordings derived from orthogonal ECG leads (X, Y, and Z), and the vector magnitude (VM) was determined in each of 3 frequency bands (200-160 Hz, 150-100 Hz and 90-50 Hz). Univariate logistic-regression analysis identified a history of hypertension, the mean and maximum energies in all frequency bands along the Z axis, the mean and maximum energies (expressed by the VM) in the 200-160 Hz frequency band, and the mean energy in the 150-100 Hz frequency band along the Y axis as predictors for post-CABG AF. Multivariate analysis identified hypertension, ejection fraction, and the maximum energies in the 90-50 Hz frequency band along the Z and composite-vector axes as independent predictors. This multivariate model had a sensitivity of 91% and a specificity of 65%. We conclude that the Morlet wavelet analysis of the P wave is a very sensitive method of identifying patients who are likely to develop AF after CABG. The occurrence of post-CABG AF can be explained by a different activation pattern along the Z axis.