Exploring and Characterizing Patient Multi-Behavior Engagement Trails and Patient Behavior Preference Patterns in the Pathway-Based mHealth Hypertension Self-Management: Analysis of Usage Data (Preprint)

2021 ◽  
Author(s):  
Dan Wu ◽  
Xiaoyuan Huyan ◽  
Yutong She ◽  
Junbin Hu ◽  
Huilong Duan ◽  
...  
Keyword(s):  
Markov Chain ◽  
High Probability ◽  
Transition Probability ◽  
Education Level ◽  
Self Management ◽  
Patient Behavior ◽  
Health Report ◽  
Preference Patterns ◽  
Behavior Preference ◽  
And Task

BACKGROUND Hypertension is a long-term medical condition. Mobile health services can help out-of-hospital patients to self-manage. However, not all management is effective, which may be because the behavior mechanism and behavior preferences of patients with various characteristics in hypertension management were unclear. OBJECTIVE The purpose of this study was to (1) explore patient multi-behavior engagement trails in the pathway-based hypertension self-management; (2) discover patient behavior preference patterns; (3) identify the characteristics of patients with different behavior preferences. METHODS This study included 863 hypertensive patients who generated 295,855 usage records in the mHealth app from December 28, 2016 to July 2, 2020. Markov Chain was used to infer the patient multi-behavior engagement trails, which contained the type, quantity, time spent, sequence, and transition probability of patient behavior. K-means algorithm was used to group patients by the normalized behavior preference features: the number of behavioral states that a patient performed in each trail. The pages in the app represented the behavior states. Chi-square tests, Z-test, analysis of variances, and Bonferroni multiple comparisons were conducted to characterize the patient behavior preference patterns. RESULTS Markov Chain analysis revealed 3 types of behavior transition (one-way transition, cycle-transition, and self-transition) and 4 trails of patient multi-behavior engagement. In perform task trail (PT-T), Patients preferred to start self-management from the states of Task BP (0.29), Task Drug (0.18), and Task Weight (0.20), and spent more time on the Task Food state (35.87s). Some patients entered the states of Task BP (0.20) and Task Drug (0.25) from the Reminder Item state. In result-oriented trail (RO-T), patients spent more energy on the Ranking state (19.66s) compared the Health Report state (13.52s). In knowledge learning trail (KL-T), there was a high probability of cycle-transition (0.47, 0.31) between the states of Knowledge List and Knowledge Content. In support acquisition trail (SA-T), there was a high probability of self-transition in the Questionnaire (0.29) state. K-means analysis discovered 3 patient behavior preference patterns: only PT-T, PT-T and KL-T, and PT-T and SA-T. There were statistically significant associations between the behavior preference pattern and gender, education level, and blood pressure (BP). CONCLUSIONS This study identified the dynamic, longitudinal, and multi-dimension characteristics of patient behavior. Patients preferred to focus on BP, medications, and weight conditions, and pay attention to BP and medications using reminders. The diet management and questionnaires were complicated and difficult to implement and record. Competitive methods such as ranking were more likely to attract patients to pay attention to their own self-management states. Female patients with lower education level and poor-controlled BP were more likely to be highly involved in hypertension health education.

scholarly journals Infinitely divisible random transition probabilities with application to dependent Markov chains

1984 ◽  
Vol 25 (4) ◽  
pp. 463-472
Author(s):  
Peter L. Chesson
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
White Noise ◽  
Transition Probabilities ◽  
Transition Probability ◽  
Transition Rates ◽  
Infinitely Divisible ◽  
Random Transition ◽  
Transition Probability Matrices ◽  
Probability Matrices

AbstractRandom transition probability matrices with stationary independent factors define “white noise” environment processes for Markov chains. Two examples are considered in detail. Such environment processes can be used to construct several Markov chains which are dependent, have the same transition probabilities and are jointly a Markov chain. Transition rates for such processes are evaluated. These results have application to the study of animal movements.

scholarly journals Characterising open clusters in the solar neighbourhood with the Tycho-Gaia Astrometric Solution

2018 ◽  
Vol 615 ◽  
pp. A49 ◽  
Author(s):  
T. Cantat-Gaudin ◽  
A. Vallenari ◽  
R. Sordo ◽  
F. Pensabene ◽  
A. Krone-Martins ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
High Probability ◽  
Open Clusters ◽  
Distance Modulus ◽  
The Sun ◽  
Proper Motions ◽  
Orbital Parameters ◽  
Naval Observatory ◽  
Galactic Potential

Context. The Tycho-Gaia Astrometric Solution (TGAS) subset of the first Gaia catalogue contains an unprecedented sample of proper motions and parallaxes for two million stars brighter than G ~ 12 mag. Aims. We take advantage of the full astrometric solution available for those stars to identify the members of known open clusters and compute mean cluster parameters using either TGAS or the fourth U.S. Naval Observatory CCD Astrograph Catalog (UCAC4) proper motions, and TGAS parallaxes. Methods. We apply an unsupervised membership assignment procedure to select high probability cluster members, we use a Bayesian/Markov Chain Monte Carlo technique to fit stellar isochrones to the observed 2MASS JHKS magnitudes of the member stars and derive cluster parameters (age, metallicity, extinction, distance modulus), and we combine TGAS data with spectroscopic radial velocities to compute full Galactic orbits. Results. We obtain mean astrometric parameters (proper motions and parallaxes) for 128 clusters closer than about 2 kpc, and cluster parameters from isochrone fitting for 26 of them located within a distance of 1 kpc from the Sun. We show the orbital parameters obtained from integrating 36 orbits in a Galactic potential.

Markov chains in small time intervals

1981 ◽  
Vol 18 (3) ◽  
pp. 747-751
Author(s):  
Stig I. Rosenlund
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Transition Probability ◽  
Small Time ◽  
Exact Order ◽  
Time Intervals ◽  
Continuous Parameter ◽  
Minimum Number

For a time-homogeneous continuous-parameter Markov chain we show that as t → 0 the transition probability pn,j (t) is at least of order where r(n, j) is the minimum number of jumps needed for the chain to pass from n to j. If the intensities of passage are bounded over the set of states which can be reached from n via fewer than r(n, j) jumps, this is the exact order.

scholarly journals Analysis of Influence Factors of Quality of Life of Lung Cancer Patients with Skin Adverse Drug Reactions Under Targeted Therapy

2022 ◽  
Author(s):  
Ruofei Du ◽  
Xin Wang ◽  
Huiyue Zhou ◽  
Lixia Ma ◽  
Leon M. Larcher ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Lung Cancer ◽  
Targeted Therapy ◽  
Adverse Drug Reactions ◽  
Education Level ◽  
Self Management ◽  
Drug Reactions ◽  
The Status ◽  
Nsclc Patients

Abstract Purpose This study was to assess the status of quality of life and explore the possible factors correlated with quality of life among non-small cell lung cancer (NSCLC) patients with skin adverse drug reactions under targeted therapy. Methods We performed a cross-sectional study including 536 NSCLC patients with skin adverse drug reactions by targeted therapy in cancer outpatient clinics of three hospitals in China between May 2020 and May 2021. And we collected data with structured questionnaires and identified the relationships among coping style, self-management and quality of life by Pearson correlation analysis and multiple linear regression algorithm. Results The total score of quality of life was 46±12.84 in 536 NSCLC patients with skin adverse drug reactions undergoing targeted therapy. In multiple linear regression analysis, we identified the significant factors associated with quality of life including age, education level, combination of medicine, Charlson Comorbidity Index (CCI), stages of disease, facing, yield, symptom management, daily activity management, psychological and emotional management, self-efficacy and self-management (P < 0.05). Conclusions NSCLC patients with skin adverse drug reactions undergoing targeted therapy generally had a compromised quality of life. And the critical factors that affected the status of quality of life were age, education level, co-morbidity, the combinatorial application of drugs and stage of disease, self-management and coping styles.

scholarly journals Relative stability and weak convergence in non-decreasing stochastically monotone Markov chains

1991 ◽  
Vol 4 (4) ◽  
pp. 293-303
Author(s):  
P. Todorovic
Keyword(s):  
Markov Chain ◽  
Weak Convergence ◽  
Markov Chains ◽  
Renewal Process ◽  
Relative Stability ◽  
Transition Probability ◽  
Markov Renewal Process

Let {ξn} be a non-decreasing stochastically monotone Markov chain whose transition probability Q(.,.) has Q(x,{x})=β(x)>0 for some function β(.) that is non-decreasing with β(x)↑1 as x→+∞, and each Q(x,.) is non-atomic otherwise. A typical realization of {ξn} is a Markov renewal process {(Xn,Tn)}, where ξj=Xn, for Tn consecutive values of j, Tn geometric on {1,2,…} with parameter β(Xn). Conditions are given for Xn, to be relatively stable and for Tn to be weakly convergent.

The censored Markov chain and the best augmentation

1996 ◽  
Vol 33 (03) ◽  
pp. 623-629 ◽  
Author(s):  
Y. Quennel Zhao ◽  
Danielle Liu
Keyword(s):  
Markov Chain ◽  
Probability Distribution ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Stationary Probability ◽  
Stationary Probability Distribution ◽  
The Best Approximation ◽  
Finite Matrix ◽  
Countable State ◽  
Stationary Probabilities

Computationally, when we solve for the stationary probabilities for a countable-state Markov chain, the transition probability matrix of the Markov chain has to be truncated, in some way, into a finite matrix. Different augmentation methods might be valid such that the stationary probability distribution for the truncated Markov chain approaches that for the countable Markov chain as the truncation size gets large. In this paper, we prove that the censored (watched) Markov chain provides the best approximation in the sense that, for a given truncation size, the sum of errors is the minimum and show, by examples, that the method of augmenting the last column only is not always the best.

MARKOV CHAIN AS A MATHEMATICAL MODEL OF BUILDING STRUCTURE WORKING UNDER LOAD

2017 ◽  
Vol 7 (1) ◽  
pp. 4-8
Author(s):  
Andrey N. DAVYDOV
Keyword(s):  
Mathematical Model ◽  
Markov Chain ◽  
Transition Matrix ◽  
Transition Probability ◽  
Probabilistic Method ◽  
External Factors ◽  
Numerical Characteristic ◽  
Building Structure ◽  
Structure Transition ◽  
Reliability Performance

Markov process as a probabilistic method for evaluation of the reliability of constructions is considered. The essence of the building structure transition from one state to another, from the infl uence of external factors is disassembled. The transition matrix as an analytical model of Markov chains to evaluate the reliability of the building structure is analyzed. Transition probability as a numerical characteristic of a mathematical model of the Markov chain is considered. A mathematical model of a building structure under load is described. Formulation of the problem to determine the assessment of the reliability performance of the building structure is proposed.

Multiple repair scenario of life cycle cost of RCC girder bridge using Markov chain model

2019 ◽  
Vol 10 (4) ◽  
pp. 75
Author(s):  
Md. Shafiqul Islam ◽  
Shayla Sharmin ◽  
Jebunnesa Islam
Keyword(s):  
Markov Chain ◽  
Life Cycle ◽  
Life Cycle Cost ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Chloride Concentration ◽  
Chain Model ◽  
Actual Performance ◽  
Girder Bridge ◽  
Inspection Data

At present, many road authorities in the world face challenges in condition monitoring diagnosis of distress and forecasting deterioration, strengthening and convalescence of aging bridge structures. The accurate prediction of the future condition is crucial for optimizing the maintenance activities. It is very tough to predict the actual performance scenario or actual in–situ structures without carrying out inspection. Limited availability of detailed inspection data is considered as one of the major drawbacks in developing deterioration models. In State Based Markov deterioration (SNMD) modelling, the main job is to estimate transition probability matrixes (TPMs). In this paper, Markov Chain Monte Carlo (MCMC) is used to estimate TPMs. In Markov Chain Model, future conditions depend on only present bridge inspection data. Multiple repair options are adopted in order to optimize life cycle cost. Repairs are needed when the critical chloride concentration exceeds 0.2. Three distinct types of cost corresponding to each repair option is considered. The objective of this paper is to minimize the life cycle cost considering appropriate repair timings of mixed repair methods. Variation of life cycle cost of five different concretes (stronger to weaker) using three different repair option is shown in this paper. For specific normalized condition of concrete’s failure probability (0.3) and specific type of concrete, variation of life cycle cost using multiple repair options is also shown in this paper.

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