O.11 Contributing factors to weight loss in chronicbronchitis and emphysema

1997 ◽  
Vol 16 ◽  
pp. 3-4
Author(s):  
E.M. Baarends ◽  
E.C. Creutzberg ◽  
E.F.M. Wouters ◽  
A.M.W.J. Schols
Keyword(s):  
Weight Loss ◽  
Contributing Factors ◽  
O 11

Management of the Geriatric Patient

2018 ◽  
Author(s):  
Michelle Martinchek
Keyword(s):  
Weight Loss ◽  
Management Strategies ◽  
Outpatient Setting ◽  
Nursing Facilities ◽  
Geriatric Syndromes ◽  
Contributing Factors ◽  
Team Members ◽  
Patients At Risk ◽  
Management Techniques ◽  
The Impact

Geriatric syndromes are complex conditions in older adults that often have many contributing factors. Examples of common geriatric syndromes include cognitive impairment, delirium, falls, frailty, weight loss, and pressure ulcers. Identifying the patients at risk for these syndromes and enacting preventive measures are also important to try to reduce the impact that many of these syndromes may have on outcomes. These syndromes can happen across many different care settings including in the community, outpatient setting, hospital, and nursing facilities. Once these syndromes are identified, management techniques often include multifactorial approaches and use both nonpharmacologic and pharmacologic means. Management strategies may include assistance from interdisciplinary team members, families, and caregivers of the patient. This review contains 30 references, 4 figures, and 4 tables. Key Words: cognition, delirium, dementia, fall, frailty, gait, geriatric, malnutrition, pressure ulcer, weight loss

scholarly journals Development and validation of an interpretable Conditional RNN for weight change prediction an obesity management mobile app (Preprint)

2020 ◽  
Author(s):  
Ho Heon Kim ◽  
Young In Kim ◽  
Yu Rang Park
Keyword(s):  
Weight Loss ◽  
Predictive Model ◽  
Mobile App ◽  
Percentage Error ◽  
Obesity Management ◽  
Contributing Factors ◽  
Negative Contribution ◽  
Actual Weight ◽  
Average Percentage ◽  
Time Invariant

BACKGROUND As an alternative to on-site obesity management, a mobile-based intervention has been given more attention. Despite the rise of mobile interventions for obesity, there are lost opportunities to achieve better outcomes due to the lack of a predictive model using currently existing health data collected longitudinally and cross-sectionally. OBJECTIVE This study aimed to develop a predictive model for weight to be used in mobile-based interventions using interpretable AI, and to explore the contributing factors to weight loss. METHODS Using lifelong of mobile application users (Noom) who used a weight loss program for 16 weeks in the U.S., an interpretable recurrent neural network for the prediction of weight after intervention considering both time-variant variables and time-invariant variables was developed. This interpretable model was trained and validated with fivefold cross-validation testing (training set: 70%; testing: 30%) using lifelog data of app users for weight loss. Mean average percent error (MAPE) between actual weight loss and predicted weight, and contribution coefficients for model interpretation. To better understand the behavior factors to weight loss or gain, the contributing factors were calculated by the contribution coefficients in test sets to interpret the effects of contributing factors to weight loss. RESULTS A total of 17,867 eligible users were included in the analysis. The overall mean average percentage error of the model was 3.50% and the errors of the model declined from 3.78% to 3.45% by observing the data at the end of the program. The time level contribution was shown to be equally distributed at 0.0625 in each week, but this gradually decreased as it approached 16 weeks. Factors such as usage pattern, weight input frequency and meal input adherence, exercise, and sharp decreases in weight trajectories had negative contribution coefficients of -0.021, -0.032, -0.015, and -0.066, respectively. As for time-invariant variables, males had a -0.091 contribution coefficient. CONCLUSIONS An interpretable artificial intelligence to utilize both data and time fixed data can forecast weight loss precisely after obesity management application while preserving model transparency. This week to week prediction model is expected to improve weight loss and provide a global explanation of contributing factors, leading to better outcomes.

Assessment of the Geriatric Patient

2018 ◽  
Author(s):  
Michelle Martinchek
Keyword(s):  
Older Adults ◽  
Weight Loss ◽  
Cognitive Impairment ◽  
Pressure Ulcer ◽  
Functional Disability ◽  
Organ System ◽  
Geriatric Syndromes ◽  
Contributing Factors ◽  
Patients At Risk ◽  
Poor Outcomes

Geriatric syndromes are complex conditions that are common in older adults and often have multiple contributing factors. These syndromes do not fit into discrete disease or organ system categories like other conditions. As the population of older adults continues to grow, it is important that providers are equipped to assess older adults for these geriatric syndromes. These syndromes are associated with functional disability and other poor outcomes. Examples of these syndromes include cognitive impairment, delirium, falls, frailty, weight loss, and pressure ulcers. Understanding the epidemiology, pathogenesis, and predisposing factors may help providers identify patients at risk for these syndromes. Furthermore, a thorough assessment is key in the evaluation of these syndromes. This review contains 48 references, 4 figures, and 8 tables. Key Words: cognition, dementia, delirium, fall, frailty, gait, geriatric, malnutrition, pressure ulcer, weight loss

Rapid Treatment of Gross Obesity by Operant Techniques

1968 ◽  
Vol 23 (2) ◽  
pp. 663-666 ◽  
Author(s):  
J. L. Bernard
Keyword(s):  
Weight Loss ◽  
Positive Reinforcement ◽  
Loss Rate ◽  
Caloric Intake ◽  
Rapid Weight Loss ◽  
Extinction Period ◽  
Endocrine Dysfunction ◽  
Contributing Factors ◽  
Rapid Treatment

This paper reports the application of operant techniques in the treatment of a case of gross obesity. The patient weighed 407 lbs. at the initiation of the program, was schizophrenic, and probably had metabolic and/or endocrine dysfunction as contributing factors. Over a period of 6 mo., of which the last 6 wk. were an extinction period, she lost 102 lbs. at a relatively stable rate. At the end of the extinction period, the loss rate had slowed somewhat but showed no indications of reversal. The rapid weight loss, compared to that in earlier studies, is attributed to positive reinforcement for weight lost, in addition to control of caloric intake.

scholarly journals GW25-e1740 Contributing Factors of Weight Regain after Weight Loss in Ahvaz, Iran

2014 ◽  
Vol 64 (16) ◽  
pp. C107
Author(s):  
Majid Karandish ◽  
Nayere Esmaeil Kaboli ◽  
Hamed Tabesh
Keyword(s):  
Weight Loss ◽  
Weight Regain ◽  
Contributing Factors

Assessment of the Geriatric Patient

2018 ◽  
Author(s):  
Michelle Martinchek
Keyword(s):  
Older Adults ◽  
Weight Loss ◽  
Cognitive Impairment ◽  
Pressure Ulcer ◽  
Functional Disability ◽  
Organ System ◽  
Geriatric Syndromes ◽  
Contributing Factors ◽  
Patients At Risk ◽  
Poor Outcomes

Geriatric syndromes are complex conditions that are common in older adults and often have multiple contributing factors. These syndromes do not fit into discrete disease or organ system categories like other conditions. As the population of older adults continues to grow, it is important that providers are equipped to assess older adults for these geriatric syndromes. These syndromes are associated with functional disability and other poor outcomes. Examples of these syndromes include cognitive impairment, delirium, falls, frailty, weight loss, and pressure ulcers. Understanding the epidemiology, pathogenesis, and predisposing factors may help providers identify patients at risk for these syndromes. Furthermore, a thorough assessment is key in the evaluation of these syndromes. This review contains 48 references, 4 figures, and 8 tables. Key Words: cognition, dementia, delirium, fall, frailty, gait, geriatric, malnutrition, pressure ulcer, weight loss

scholarly journals Cachexia in Patients with Gastrointestinal Cancers: Contributing Factors, Prevention, and Current Management Approaches

2020 ◽  
pp. 62-70
Author(s):  
Oliver Grundmann ◽  
Saunjoo L. Yoon ◽  
Joseph J. Williams
Keyword(s):  
Amino Acids ◽  
Weight Loss ◽  
Cancer Cachexia ◽  
Body Weight Loss ◽  
Essential Amino Acids ◽  
Contributing Factors ◽  
Reactive Protein ◽  
Risk Of Mortality ◽  
Short Period ◽  
Management Approaches

Cancer cachexia is highly prevalent among patients with the advanced stage of cancers and leads to a higher risk of mortality. Delayed management of cachexia results in suboptimal treatment outcomes and irreversible progression to refractory cachexia. The purpose of this review is to provide the pathophysiology of cancer cachexia, emerging diagnostic criteria with potential biomarkers, prevention strategies, and novel treatment approaches. Cachexia is characterised by the presence of an inflammatory process in conjunction with muscle mass and unintentional body weight loss. Various biomarkers such as leptin, ghrelin, TNFα, essential amino acids, total amino acids, and C-reactive protein are indicative of cachexia. Increased circulating levels of β-dystroglycan, myosin heavy-chain, and dystrophin are indicators of shortened survival time as skeletal muscle tissues break down. Despite muscle wasting being a hallmark of cachexia, recommended cachexia management is limited to nutritional counselling and administration of an appetite stimulant and corticosteroids for a short period, which often fail to reverse cancer cachexia. It is critical to monitor weight loss using the cachexia grading system for early detection, to halt progression to refractory cachexia and improve the survival of patients with cancer cachexia.

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