Conceptualization of a personalized ecoach for wellness promotion

Gerdes, Martin; Martínez, Santiago; Tjondronegoro, Dian

doi:10.1145/3154862.3154930

Cited by 17 publications

(21 citation statements)

References 28 publications

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“…Presumably due to individually different physical activity patterns, different algorithms and parameters have to be considered. As a starting point, we selected the algorithms based on well-established sources [ 41 , 42 ], applied cross-validation, and grid-searched the values of the selected parameters. Nevertheless, it’s important to note that these algorithms, parameters, and grid search values might not work best on all individual physical patterns, and the algorithms, parameters, and grid search values should only be used as starting points.…”

Section: Discussionmentioning

confidence: 99%

“…In order to improve physical activity in combination with activity trackers, a coaching feature is helpful, but only when the messages are personal and placed in context [ 40 ]. Perceiving the coaching information as personal and relevant is crucial for the effectiveness of (e)Coaching [ 41 ]. Such tailored (e)Coaching has many aspects, two of which are personalization and timing [ 42 ].…”

Section: Related Workmentioning

confidence: 99%

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Personalized Physical Activity Coaching: A Machine Learning Approach

Dijkhuis

Blaauw

Ittersum

et al. 2018

Sensors

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Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant’s progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Personalized Physical Activity Coaching: A Machine Learning Approach

Dijkhuis

Blaauw

Ittersum

et al. 2018

Sensors

View full text Add to dashboard Cite

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“…eCoaching has other possibilities when compared to traditional coaching in terms of value addition, performance, and competence. Efficacy [52] study is a problem in both kinds of coaching to date, as revealed in the systematic literature review, for the following reasons: insufficient planning in study selection and study design [1,23], lack of conceptual/contextual clarity [24,25], inappropriate selection of sample size for statistical analysis [61], dearth of proper background education [74], lack of reliance and self-disclosure [1,22], absence of variation in a selected population [22,61], and lack of competence and experience with technology (digital illiteracy) [22,75].…”

Section: Rq5: How Can Ecoaching Promote a Healthy Lifestyle With Proven Coaching Methods Using Ict?mentioning

confidence: 99%

Human Coaching Methodologies for Automatic Electronic Coaching (eCoaching) as Behavioral Interventions With Information and Communication Technology: Systematic Review (Preprint)

Chatterjee¹,

Gerdes²,

Prinz³

et al. 2020

Preprint

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BACKGROUND We review conventional human coaching methodologies following a systematic literature review approach to identify coaching methods, approaches, and components relevant for human coaching. Then, we learn how those identified methods, approaches, and components can be utilized to improve an electronic coaching (eCoaching) process for the promotion of a healthy lifestyle with the support of information and communication technologies (ICT). OBJECTIVE This study aims at the identification of existing human coaching methodologies for behavior intervention and their applicability for healthy lifestyle management utilizing ICT. METHODS We performed a systematic literature review, as proposed by Wendler, to search the scientific databases EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MDPI, Google Scholar, and PubMed for publications that included personal coaching (from 2000 to 2019), persuasive eCoaching, and behavior intervention for a healthy lifestyle (from 2014 to 2019). The PRISMA framework was used for evidence-based systematic review and meta-analysis. RESULTS The systematic search resulted in 54 publications, including 47 papers and 7 books. 33 of those publications were related to behavior intervention eCoaching, and the remaining 21 were on human coaching. The most utilized persuasive eCoaching components were intervention (n=10) , personalization (n=8), interaction and co-creation (n=7), goal setting and evaluation (n=6), automation (n=6), and persuasion (n=4). The most relevant aspects for human coaching were behavior (n=18), methodology (n=5), psychology (n=4), and mentoring (n=4). In this study, we have focused on the principles of coaching methodologies to understand the psychology, behavioral science, coaching philosophy, essential coaching components for effective coaching, and associated challenges related to coaching. We have discussed how we can integrate the obtained knowledge into the eCoaching process for healthy lifestyle management using ICT. The identified principles of coaching, relevant for eCoaching were knowledge, coaching skill, observation, interaction, ethics, trust, efficacy study, coaching experience, pragmatism, intervention, goal setting, and evaluation. CONCLUSIONS This systematic literature review selected principles, associated strengths, and limitations for behavior intervention from established coaching methods. The identified principles of coaching point towards integrating human psychology in eCoaching to develop effective intervention plans for healthy lifestyle management and overcome existing limitations. It serves as a basis for further research with a focus on designing, development, testing, and evaluation of an intelligent eCoach system's performance for the automatic generation of meaningful, evidence-based, context-specific, personalized recommendations to achieve personal wellness goals

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“…An electronic coaching ("eCoaching") system can empower people to manage a healthy lifestyle with early risk predictions and appropriate individualized recommendations. To develop an intelligent eCoach system for automated, personalized, contextual, and behavioral recommendations to achieve personal wellness goals, addressing obesity as a study case, we propose to (a) identify associated health risk factors, (b) perform data collection from identified controlled trials, (c) analyze the data, and (d) perform a predictive analysis with machine learning algorithms for future health risk predictions and behavioral interventions [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Our primary focus was obesity/overweight in adults. In this paper, we used the term "eCoaching" [10,11], which is our future research focus for behavioral intervention for the promotion of a healthy lifestyle targeting "obesity and overweight" as a study case. "eCoaching" as such is also not in the scope of this paper.…”

Section: Introductionmentioning

confidence: 99%

Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

Chatterjee

Gerdes

Martínez

2020

Sensors

152

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Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and “obesity/overweight” is one of the consequences. “Obesity and overweight” are one of the major lifestyle diseases that leads to other health conditions, such as cardiovascular diseases (CVDs), chronic obstructive pulmonary disease (COPD), cancer, diabetes type II, hypertension, and depression. It is not restricted within the age and socio-economic background of human beings. The “World Health Organization” (WHO) has anticipated that 30% of global death will be caused by lifestyle diseases by 2030 and it can be prevented with the appropriate identification of associated risk factors and behavioral intervention plans. Health behavior change should be given priority to avoid life-threatening damages. The primary purpose of this study is not to present a risk prediction model but to provide a review of various machine learning (ML) methods and their execution using available sample health data in a public repository related to lifestyle diseases, such as obesity, CVDs, and diabetes type II. In this study, we targeted people, both male and female, in the age group of >20 and <60, excluding pregnancy and genetic factors. This paper qualifies as a tutorial article on how to use different ML methods to identify potential risk factors of obesity/overweight. Although institutions such as “Center for Disease Control and Prevention (CDC)” and “National Institute for Clinical Excellence (NICE)” guidelines work to understand the cause and consequences of overweight/obesity, we aimed to utilize the potential of data science to assess the correlated risk factors of obesity/overweight after analyzing the existing datasets available in “Kaggle” and “University of California, Irvine (UCI) database”, and to check how the potential risk factors are changing with the change in body-energy imbalance with data-visualization techniques and regression analysis. Analyzing existing obesity/overweight related data using machine learning algorithms did not produce any brand-new risk factors, but it helped us to understand: (a) how are identified risk factors related to weight change and how do we visualize it? (b) what will be the nature of the data (potential monitorable risk factors) to be collected over time to develop our intended eCoach system for the promotion of a healthy lifestyle targeting “obesity and overweight” as a study case in the future? (c) why have we used the existing “Kaggle” and “UCI” datasets for our preliminary study? (d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics?

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Conceptualization of a personalized ecoach for wellness promotion

Cited by 17 publications

References 28 publications

Personalized Physical Activity Coaching: A Machine Learning Approach

Personalized Physical Activity Coaching: A Machine Learning Approach

Human Coaching Methodologies for Automatic Electronic Coaching (eCoaching) as Behavioral Interventions With Information and Communication Technology: Systematic Review (Preprint)

Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

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