López-Jiménez, Francisco

Abstract Background Older persons with obesity aged 65+ residing in rural areas have reduced access to weight management programs due to geographic isolation. The ability to integrate technology into health promotion interventions shows a potential to reach this underserved population. Methods A 12-week pilot in 28 older rural adults with obesity (body mass index [BMI] ≥ 30 kg/m2) was conducted at a community aging center. The intervention consisted of individualized, weekly dietitian visits focusing on behavior therapy and caloric restriction with twice weekly physical therapist-led group strengthening training classes in a community-based aging center. All participants were provided a Fitbit Flex 2. An aerobic activity prescription outside the strength training classes was provided. Results Mean age was 72.9 ± 5.3 years (82% female). Baseline BMI was 37.1 kg/m2, and waist circumference was 120.0 ± 33.0 cm. Mean weight loss (pre/post) was 4.6 ± 3.2 kg (4.9 ± 3.4%; p < .001). Of the 40 eligible participants, 33 (75%) enrolled, and the completion rate was high (84.8%). Objective measures of physical function improved at follow-up: 6-minute walk test improved: 35.7 ± 41.2 m (p < .001); gait speed improved: 0.10 ± 0.24 m/s (p = .04); and five-times sit-to-stand improved by 2.1 seconds (p < .001). Subjective measures of late-life function improved (5.2 ± 7.1 points, p = .003), as did Patient-Reported Outcome Measurement Information Systems mental and physical health scores (5.0 ± 5.7 and 4.4 ± 5.0, both p < .001). Participants wore their Fitbit 93.9% of all intervention days, and were overall satisfied with the trial (4.5/5.0, 1–5 low–high) and with Fitbit (4.0/5.0). Conclusions A multicomponent obesity intervention incorporating a wearable device is feasible and acceptable to older adults with obesity, and potentially holds promise in enhancing health.

Importance. Obesity is a worsening epidemic worldwide. Effective and accessible weight loss programs to combat obesity on a large scale are warranted, but a need for frequent face-to-face care might impose a limitation. Objective. To evaluate whether individuals following a weight loss program based on a mobile application, wireless scale, and nutritional program but no face-to-face care can achieve clinically significant weight loss in a large cohort. Design. Retrospective observational analysis. Setting. China from October 2016 to December 2017. Participants. Mobile application users with a minimum of 2 weights (baseline and ≥35 days). Intervention. A commercial (Weijian Technologies) weight loss program consisting of a dietary replacement, self-monitoring using a wireless home scale, and frequent guidance via mobile application. Main Outcome. Mean weight change around 42, 60, 90, and 120 days after program initiation with subgroup analysis by gender, age, and frequency of use. Results. 251,718 individuals, with a mean age of 37.3 years (SD: 9.86) (79% female), were included with a mean weight loss of 4.3 kg (CI: ±0.02) and a mean follow-up of 120 days (SD: 76.8 days). Mean weight loss at 42, 60, 90, and 120 d was 4.1 kg (CI: ±0.02), 4.9 kg (CI: ±0.02), 5.6 kg (CI: ±0.03), and 5.4 kg (CI: ±0.04), respectively. At 120 d, 62.7% of participants had lost at least 5% of their initial weight. Both genders and all usage frequency tertiles showed statistically significant weight loss from baseline at each interval (P<0.001), and this loss was greater in men than in women (120 d: 6.5 vs. 5.2 kg; P<0.001). The frequency of recording (categorized as high-, medium-, or low-frequency users) was associated with greater weight loss when comparing high, medium, and low tertile use groups at all time intervals investigated (e.g., 120 d: −8.6, −5.6, and −2.2 kg, respectively; P<0.001). Conclusions. People following a commercially available hybrid weight loss program using a mobile application, wireless scale, and nutritional program without face-to-face interaction on average achieved clinically significant short- and midterm weight loss. These results support the implementation of comparable technologies for weight control in a large population.

Introduction Severe obesity is a growing epidemic that causes significant morbidity and mortality, and is particularly difficult to reverse. Efficacious and cost-effective interventions are needed to combat this epidemic. This study hypothesized that obese people (body mass index (BMI) ≥35 kg/m 2 ) using a remote weight-loss program combining a mobile application, wireless scales, and low-calorie meal replacement would experience clinically significant weight loss. Methods This study was a retrospective observational analysis of 8275 individuals with a baseline BMI ≥35 kg/m 2 who used a remote weight-loss program combining mobile applications, frequent self-weighing, and calorie restriction via meal replacement for a minimum of 35 days. Weight changes were evaluated at multiple intervals (42, 60, 90, and 120 days), and weight loss was evaluated for all and for pre-specified subgroups based on demographic features and frequency of self-weighing. Results Mean weight loss at 42 days ( N = 6781) was 8.1 kg (margin of error (MOE) = 0.126 kg) with 73.6% of users experiencing >5% total body weight loss. Both men (9.1 kg; MOE = 0.172 kg; 7.9% from baseline) and women (7.1 kg; MOE = 0.179 kg; 7.2% from baseline) experienced significant weight loss. At the 120-day interval ( N = 2914), mean weight loss was 14 kg (MOE = 0.340 kg), 13% total body weight loss from baseline, and 82.3% of participants had lost >5% of their initial body weight. The decrease in body-fat percent correlated well with weight loss ( R = 0.92; p < 0.001). Conclusions In a large cohort of individuals with class II or III obesity, a remote weight-loss program combining mobile applications, daily self-weighing, and calorie restriction via meal replacement resulted in dramatic weight loss among subjects who were active users when evaluated through a retrospective observational analysis.

Introduction: Metabolic Syndrome represents a constellation of findings known to increase cardiovascular risk and all-cause mortality and is becoming more frequent in China. Smartphone applications may provide a cost-effective method to engage in weight loss and improve metabolic syndrome components. Methods: Prospective study of changes in weight and metabolic syndrome risk factors at 6 weeks and 6 months using a smartphone application utilizing a wireless scale, meal replacement biscuits and e-coaching in China. All participants had or were at high risk for metabolic syndrome and all interaction was online, without face to face contact. Results: 218 individuals ,55% female, participated. Median weight loss at 6 weeks and 6 months was 7.3kg and 8.4 kg (P<0.001) respectively. At baseline, 60% met a diagnosis of metabolic syndrome this dropped to 38% at 6 weeks (P<0.001) and stayed at 38% at 6 months (P<0.001). At 6 week the prevalence of all components of metabolic syndrome were improved (P<0.05) except HDL. Conclusions: A smartphone weight loss platform that utilizes a wireless scale, meal replacement biscuits and e-coaching was associated with significant weight loss and improvement in several metabolic syndrome components at 6 weeks and 6 months. This program may provide a scalable intervention against metabolic syndrome, especially in developing countries such as China.

Background: Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1) assess whether the performance of a deep learning algorithm designed to detect low left ventricular ejection fraction using the 12-lead ECG varies by race/ethnicity and to (2) determine whether its performance is determined by the derivation population or by racial variation in the ECG. Methods: We performed a retrospective cohort analysis that included 97 829 patients with paired ECGs and echocardiograms. We tested the model performance by race/ethnicity for convolutional neural network designed to identify patients with a left ventricular ejection fraction ≤35% from the 12-lead ECG. Results: The convolutional neural network that was previously derived in a homogeneous population (derivation cohort, n=44 959; 96.2% non-Hispanic white) demonstrated consistent performance to detect low left ventricular ejection fraction across a range of racial/ethnic subgroups in a separate testing cohort (n=52 870): non-Hispanic white (n=44 524; area under the curve [AUC], 0.931), Asian (n=557; AUC, 0.961), black/African American (n=651; AUC, 0.937), Hispanic/Latino (n=331; AUC, 0.937), and American Indian/Native Alaskan (n=223; AUC, 0.938). In secondary analyses, a separate neural network was able to discern racial subgroup category (black/African American [AUC, 0.84], and white, non-Hispanic [AUC, 0.76] in a 5-class classifier), and a network trained only in non-Hispanic whites from the original derivation cohort performed similarly well across a range of racial/ethnic subgroups in the testing cohort with an AUC of at least 0.930 in all racial/ethnic subgroups. Conclusions: Our study demonstrates that while ECG characteristics vary by race, this did not impact the ability of a convolutional neural network to predict low left ventricular ejection fraction from the ECG. We recommend reporting of performance among diverse ethnic, racial, age, and sex groups for all new artificial intelligence tools to ensure responsible use of artificial intelligence in medicine.

The term “obesity” is associated with societal stigma and discrimination. Eight individual semi-structured interviews and five focus groups with 29 community-dwelling, rural older adults with obesity, seven primary care clinicians, and four rural community leaders were completed using purposive and snowball sampling. Clinicians perceived that older adults are less affected by obesity stigma than younger adults, yet this was not observed by community leaders; however, older participants with obesity reported that they often felt ashamed and/or stigmatized because of their weight. There was also a disconnect between clinician and older adult understanding of obesity. For older adults with obesity, the word “obesity” was associated with negative connotations. Just as physiological aspects of obesity persist into older adulthood, so do psychological aspects, such as perceptions of stigma. The use of the word “obesity” in medical settings may hinder communication between clinician and older participants. Heightened awareness may change the dialogue around obesity.

Body mass index (BMI) does not differentiate fat and lean mass or the distribution of adipose tissue. The purpose of this study was to examine the prevalence of metabolic syndrome (MetS) among patients entering outpatient cardiac rehabilitation (CR) across fat mass index (FMI) categories compared with BMI. This retrospective cross-sectional study evaluated dual-energy x-ray absorptiometry in 483 CR patients from 1 January 2014, through 31 December 2017. Clinical data were extracted from the electronic health record. Patients were grouped by FMI and BMI categories. Mean (SD) age of patients was 64.3 (14) years. The normal FMI category had 15 patients; excess fat, 74; and obese, 384. In contrast, 93, 174, and 216 were in the normal, overweight, and obese BMI categories, respectively. Prevalence of MetS was 0 (0%) in normal, 5 (1%) in excess fat, and 167 (54%) in obese FMI, with 97% in the obese category. MetS prevalence was 4 patients (0.8%) in normal, 39 (8%) in overweight, and 129 (27%) in obese BMI categories, with 75% of MetS in the obese category. FMI more accurately classified CR patients with metabolically abnormal fat (p < 0.001). FMI is a more sensitive index than BMI for metabolically abnormal fat of outpatient CR patients.

Background: Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that a convolutional neural network (CNN) could be trained through a process called deep learning to predict a person’s age and self-reported sex using only 12-lead ECG signals. We further hypothesized that discrepancies between CNN-predicted age and chronological age may serve as a physiological measure of health. Methods: We trained CNNs using 10-second samples of 12-lead ECG signals from 499 727 patients to predict sex and age. The networks were tested on a separate cohort of 275 056 patients. Subsequently, 100 randomly selected patients with multiple ECGs over the course of decades were identified to assess within-individual accuracy of CNN age estimation. Results: Of 275 056 patients tested, 52% were males and mean age was 58.6±16.2 years. For sex classification, the model obtained 90.4% classification accuracy with an area under the curve of 0.97 in the independent test data. Age was estimated as a continuous variable with an average error of 6.9±5.6 years (R-squared =0.7). Among 100 patients with multiple ECGs over the course of at least 2 decades of life, most patients (51%) had an average error between real age and CNN-predicted age of <7 years. Major factors seen among patients with a CNN-predicted age that exceeded chronologic age by >7 years included: low ejection fraction, hypertension, and coronary disease ( P <0.01). In the 27% of patients where correlation was >0.8 between CNN-predicted and chronologic age, no incident events occurred over follow-up (33±12 years). Conclusions: Applying artificial intelligence to the ECG allows prediction of patient sex and estimation of age. The ability of an artificial intelligence algorithm to determine physiological age, with further validation, may serve as a measure of overall health.

Objective Cardiac rehabilitation (CR) availability, programme characteristics and barriers are not well-known in low/middle-income countries (LMICs). In this study, they were compared with high-income countries (HICs) and by CR funding source. Methods A cross-sectional online survey was administered to CR programmes globally. Need for CR was computed using incident ischaemic heart disease (IHD) estimates from the Global Burden of Disease study. General linear mixed models were performed. Results CR was identified in 55/138 (39.9%) LMICs; 47/55 (85.5% country response rate) countries participated and 335 (53.5% programme response) surveys were initiated. There was one CR spot for every 66 IHD patients in LMICs (vs 3.4 in HICs). CR was most often paid by patients in LMICs (n=212, 65.0%) versus government in HICs (n=444, 60.2%; p<0.001). Over 85% of programmes accepted guideline-indicated patients. Cardiologists (n=266, 89.3%), nurses (n=234, 79.6%; vs 544, 91.7% in HICs, p=0.001) and physiotherapists (n=233, 78.7%) were the most common providers on CR teams (mean=5.8±2.8/programme). Programmes offered 7.3±1.8/10 core components (vs 7.9±1.7 in HICs, p<0.01) over 33.7±30.7 sessions (significantly greater in publicly funded programmes; p<0.001). Publicly funded programmes were more likely to have social workers and psychologists on staff, and to offer tobacco cessation and psychosocial counselling. Conclusion CR is only available in 40% of LMICs, but where offered is fairly consistent with guidelines. Governments should enact policies to reimburse CR so patients do not pay out-of-pocket.