scholarly journals Social Support and Common Dyadic Coping in Couples' Dyadic Management of Type II Diabetes: Protocol for an Ambulatory Assessment Application

10.2196/13685 ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. e13685 ◽  
Author(s):  
Janina Lüscher ◽  
Tobias Kowatsch ◽  
George Boateng ◽  
Prabhakaran Santhanam ◽  
Guy Bodenmann ◽  
...  
Keyword(s):  
Social Support ◽  
Health Behavior ◽  
Everyday Life ◽  
Type Ii Diabetes ◽  
Well Being ◽  
Sensor Data ◽  
Second Phase ◽  
Illness Management ◽  
Type Ii ◽  
Invisible Support

Background Type II diabetes mellitus (T2DM) is a common chronic disease. To manage blood glucose levels, patients need to follow medical recommendations for healthy eating, physical activity, and medication adherence in their everyday life. Illness management is mainly shared with partners and involves social support and common dyadic coping (CDC). Social support and CDC have been identified as having implications for people’s health behavior and well-being. Visible support, however, may also be negatively related to people’s well-being. Thus, the concept of invisible support was introduced. It is unknown which of these concepts (ie, visible support, invisible support, and CDC) displays the most beneficial associations with health behavior and well-being when considered together in the context of illness management in couple’s everyday life. Therefore, a novel ambulatory assessment application for the open-source behavioral intervention platform MobileCoach (AAMC) was developed. It uses objective sensor data in combination with self-reports in couple’s everyday life. Objective The aim of this paper is to describe the design of the Dyadic Management of Diabetes (DyMand) study, funded by the Swiss National Science Foundation (CR12I1_166348/1). The study was approved by the cantonal ethics committee of the Canton of Zurich, Switzerland (Req-2017_00430). Methods This study follows an intensive longitudinal design with 2 phases of data collection. The first phase is a naturalistic observation phase of couples’ conversations in combination with experience sampling in their daily lives, with plans to follow 180 T2DM patients and their partners using sensor data from smartwatches, mobile phones, and accelerometers for 7 consecutive days. The second phase is an observational study in the laboratory, where couples discuss topics related to their diabetes management. The second phase complements the first phase by focusing on the assessment of a full discussion about diabetes-related concerns. Participants are heterosexual couples with 1 partner having a diagnosis of T2DM. Results The AAMC was designed and built until the end of 2018 and internally tested in March 2019. In May 2019, the enrollment of the pilot phase began. The data collection of the DyMand study will begin in September 2019, and analysis and presentation of results will be available in 2021. Conclusions For further research and practice, it is crucial to identify the impact of social support and CDC on couples’ dyadic management of T2DM and their well-being in daily life. Using AAMC will make a key contribution with regard to objective operationalizations of visible and invisible support, CDC, physical activity, and well-being. Findings will provide a sound basis for theory- and evidence-based development of dyadic interventions to change health behavior in the context of couple’s dyadic illness management. Challenges to this multimodal sensor approach and its feasibility aspects are discussed. International Registered Report Identifier (IRRID) PRR1-10.2196/13685

scholarly journals Social Support and Common Dyadic Coping in Couples' Dyadic Management of Type II Diabetes: Protocol for an Ambulatory Assessment Application (Preprint)

2019 ◽  
Author(s):  
Janina Lüscher ◽  
Tobias Kowatsch ◽  
George Boateng ◽  
Prabhakaran Santhanam ◽  
Guy Bodenmann ◽  
...  
Keyword(s):  
Social Support ◽  
Health Behavior ◽  
Everyday Life ◽  
Type Ii Diabetes ◽  
Well Being ◽  
Sensor Data ◽  
Second Phase ◽  
Illness Management ◽  
Type Ii ◽  
Invisible Support

BACKGROUND Type II diabetes mellitus (T2DM) is a common chronic disease. To manage blood glucose levels, patients need to follow medical recommendations for healthy eating, physical activity, and medication adherence in their everyday life. Illness management is mainly shared with partners and involves social support and common dyadic coping (CDC). Social support and CDC have been identified as having implications for people’s health behavior and well-being. Visible support, however, may also be negatively related to people’s well-being. Thus, the concept of invisible support was introduced. It is unknown which of these concepts (ie, visible support, invisible support, and CDC) displays the most beneficial associations with health behavior and well-being when considered together in the context of illness management in couple’s everyday life. Therefore, a novel ambulatory assessment application for the open-source behavioral intervention platform MobileCoach (AAMC) was developed. It uses objective sensor data in combination with self-reports in couple’s everyday life. OBJECTIVE The aim of this paper is to describe the design of the Dyadic Management of Diabetes (DyMand) study, funded by the Swiss National Science Foundation (CR12I1_166348/1). The study was approved by the cantonal ethics committee of the Canton of Zurich, Switzerland (Req-2017_00430). METHODS This study follows an intensive longitudinal design with 2 phases of data collection. The first phase is a naturalistic observation phase of couples’ conversations in combination with experience sampling in their daily lives, with plans to follow 180 T2DM patients and their partners using sensor data from smartwatches, mobile phones, and accelerometers for 7 consecutive days. The second phase is an observational study in the laboratory, where couples discuss topics related to their diabetes management. The second phase complements the first phase by focusing on the assessment of a full discussion about diabetes-related concerns. Participants are heterosexual couples with 1 partner having a diagnosis of T2DM. RESULTS The AAMC was designed and built until the end of 2018 and internally tested in March 2019. In May 2019, the enrollment of the pilot phase began. The data collection of the DyMand study will begin in September 2019, and analysis and presentation of results will be available in 2021. CONCLUSIONS For further research and practice, it is crucial to identify the impact of social support and CDC on couples’ dyadic management of T2DM and their well-being in daily life. Using AAMC will make a key contribution with regard to objective operationalizations of visible and invisible support, CDC, physical activity, and well-being. Findings will provide a sound basis for theory- and evidence-based development of dyadic interventions to change health behavior in the context of couple’s dyadic illness management. Challenges to this multimodal sensor approach and its feasibility aspects are discussed. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/13685

scholarly journals Contributing Factors in Adolescents’ Mental Well-Being—The Role of Socioeconomic Status, Social Support, and Health Behavior

2020 ◽  
Vol 12 (22) ◽  
pp. 9597
Author(s):  
Gabriella Nagy-Pénzes ◽  
Ferenc Vincze ◽  
Éva Bíró
Keyword(s):  
Social Support ◽  
Socioeconomic Status ◽  
Life Satisfaction ◽  
Health Behavior ◽  
Mental Health Problems ◽  
Well Being ◽  
Cross Sectional ◽  
Unhealthy Eating ◽  
The Impact ◽  
Mental Well Being

Mental disorders are common in adolescents, and for effective interventions we should be aware of their determinants. However, there are only a small number of studies investigating the combined effect of multiple factors. Therefore, our aim is to assess the impact of socioeconomic status, social support, and health behavior on adolescents’ mental well-being. A cross-sectional health survey of 1641 children was carried out in accordance with the study protocol of the Hungarian Health Behavior in School-aged Children survey. Multivariate multiple regression was used to analyze the main determinants of mental well-being. The boys’ mental well-being was favorable compared to girls; lower subjective family wealth was associated with lower life satisfaction and depressive mood. Life satisfaction was positively related to healthy eating, social support, and physical activity. Unhealthy eating, sedentary lifestyle, and lower social support were associated with higher depression scores. Higher social support reduces psychosomatic symptoms, while unhealthy eating and spending a lot of time in front of the computer increase them. Both social support and healthy lifestyle seem to be protective against mental health problems among adolescents, and thus interventions should focus on these factors regardless of the socioeconomic status of the participants, with special attention given to girls.

scholarly journals Examination of Well-being in Patients with Type II Diabetes

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Büşra Kayaaslan ◽  
Neslihan Lok
Keyword(s):  
Type Ii Diabetes ◽  
Well Being ◽  
Type Ii

Internet of Things in E-Health

2017 ◽  
pp. 191-197 ◽  
Author(s):  
Branka Rodić Trmčić ◽  
Aleksandra Labus ◽  
Svetlana Mitrović ◽  
Vesna Buha ◽  
Gordana Stanojević
Keyword(s):  
Internet Of Things ◽  
Health Behavior ◽  
Well Being ◽  
Sensor Data ◽  
Main Task ◽  
Preventive Actions ◽  
Framework Model ◽  
The Impact ◽  
Health And Well Being ◽  
Improve Health

The main task of Internet of Things in eHealth solutions is to collect data, connect people, things and processes. This provides a wealth of information that can be useful in decision-making, improving health and well-being. The aim of this study is to identify framework of sensors and application health services to detect sources of stress and stressors and make them visible to users. Also, we aim at extracting relationship between event and sensor data in order to improve health behavior. Evaluation of the proposed framework model will be performed. Model is based on Internet of Things in eHealth and is going to aim to improve health behavior. Following the established pattern of behavior realized through wearable system users will be proposed a preventive actions model. Further, it will examine the impact of changing health behavior on habits, condition and attitudes in relation to well-being and prevention.

scholarly journals Students’ attitudes on personal drug selection exercise in writing prescription

2017 ◽  
Vol 7 (1) ◽  
pp. 147
Author(s):  
Manish Kumar ◽  
Lalit Mohan ◽  
Hitesh Mishra ◽  
Akash Chandra ◽  
Harihar Dikshit
Keyword(s):  
Diabetes Mellitus ◽  
Type Ii Diabetes ◽  
Type Ii Diabetes Mellitus ◽  
Second Phase ◽  
Type Ii ◽  
Drug Selection ◽  
Medical Specialties ◽  
Third Phase ◽  
Standard Text ◽  
Students Attitudes

Background: This study was developed to know the students’ views regarding personal drug (P-drug) concept in rational prescription of drugs and also giving them training of creating and using personal drug concept.Methods: 40 medical students (5th semester) divided in four groups were involved voluntarily in a three phase, questionnaire based and prospective study. In first and second phase students were taught and asked to derive P-drug using different standard text books and Current Index of Medical Specialties (CIMS) by analyzing efficacy, safety, cost and convenience of drugs used for type II diabetes mellitus. Third phase was designed to know the students’ perception regarding the exercise and difficulties faced in the process of P-drug selection. It contained demographic and 12 questions with answer using Likert scale.Results: Students selected biguanide (metformin) as a P-drug in terms of efficacy, safety, cost and convenience. 95% (36 out of 40) responded in the questionnaire, out of which 92% (33 out of 36) had given answer with mean score ≥4. Overall median score was 4 and Interquartile Range was 4-5. 89% (32) strongly agreed that P-dug selection teaching helped them to understand pharmacology better. Majority (83% or 30) were in favour of introducing P-drug selection exercises in undergraduate pharmacology curriculum.Conclusions: P-drug selection exercise helped students to understand the differences among various drugs used for the treatment of type II diabetes mellitus and given them a strong foundation for developing rational use of the medicine in their future career as a doctor.

Internet of Things in E-Health

2018 ◽  
pp. 880-885
Author(s):  
Branka Rodić Trmčić ◽  
Aleksandra Labus ◽  
Svetlana Mitrović ◽  
Vesna Buha ◽  
Gordana Stanojević
Keyword(s):  
Internet Of Things ◽  
Health Behavior ◽  
Well Being ◽  
Sensor Data ◽  
Main Task ◽  
Preventive Actions ◽  
Framework Model ◽  
The Impact ◽  
Health And Well Being ◽  
Improve Health

The main task of Internet of Things in eHealth solutions is to collect data, connect people, things and processes. This provides a wealth of information that can be useful in decision-making, improving health and well-being. The aim of this study is to identify framework of sensors and application health services to detect sources of stress and stressors and make them visible to users. Also, we aim at extracting relationship between event and sensor data in order to improve health behavior. Evaluation of the proposed framework model will be performed. Model is based on Internet of Things in eHealth and is going to aim to improve health behavior. Following the established pattern of behavior realized through wearable system users will be proposed a preventive actions model. Further, it will examine the impact of changing health behavior on habits, condition and attitudes in relation to well-being and prevention.

RELATIONSHIP BETWEEN QUALITY OF LIFE AND MENTAL HEALTH OF PEOPLE WITH TYPE II DIABETES IN RURALAREAS

2021 ◽  
pp. 35-37
Author(s):  
Selvakumar Jagannathan ◽  
Kannan Ramiah ◽  
Valarmathy Selvakumar
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Type Ii Diabetes ◽  
Well Being ◽  
World Health ◽  
Mental Condition ◽  
Type Ii ◽  
The Impact ◽  
The Relationship

Background:For populations with chronic disease, measurement of QOLprovides a meaningful way to determine the impact of health care when cure is not possible. Revicki and colleagues (2000) dene QOL as "a broad range of human experiences related to one's overall well-being. It implies value based on subjective functioning in comparison with personal expectations and is dened by subjective experiences, states and perceptions. The World Health Organization (2010) denes mental health as a state of positive mental condition in which one realizes his/her capabilities, manages the life stresses, put effort effectively and efciently, and is competent enough to put some contribution to his/her society. According to mental health model (Veit & Ware, 1983), there are two components of mental health, rst is psychological well-being and the other is psychological distress. Therefore, studying the relationship between quality of life and mental health of People with type II diabetes will reveal that to what extend a good quality of life have a relationship in maintaining better mental health in order to cope up with diabetes complications. Objective:The present study was undertaken to know the relationship between quality of life and mental health of people with Type II diabetes. Sample: 30 Type II diabetes were selected from the Diabetes Management Clinic in Rural areas for the assessment of quality of life and mental health. Methodology:The quality of life was assessed using “The Quality of Life Scale (QOLS)” by John Flanagan (1970) and Mental health was assessed using “Mental health inventory (MHI)-18 items by Veit and ware (1983). Finding and Conclusion: The study revealed that there is a signicant relationship between quality of life and mental health of people with Type II diabetes

scholarly journals Development and psychometric properties of a new social support scale for self-care in middle-aged patients with type II diabetes (S4-MAD)

2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Shohreh Naderimagham ◽  
Shamsaddin Niknami ◽  
Farid Abolhassani ◽  
Ebrahim Hajizadeh ◽  
Ali Montazeri
Keyword(s):  
Social Support ◽  
Psychometric Properties ◽  
Type Ii Diabetes ◽  
Self Care ◽  
Type Ii ◽  
Middle Aged ◽  
Aged Patients ◽  
Social Support Scale
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