Sensor based coaching for a fit and healthy society

Varadharajan, Vijay; Kannav, V.; Pasala, Anjaneyulu

doi:10.1109/istas.2016.7764275

Cited by 4 publications

(6 citation statements)

References 5 publications

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“…The specification of the goals differed across the systems and apps. The goals could be specified in terms of game level [80], training zone and HR [52,75,81], activity duration [3,43,76,81,82,89], step data [2,54,60,93,95], or activity level prescription by an expert [23]. These goals were adapted according to the person’s status and did not follow standardized fixed goals (such as “30 minutes of MVPA”).…”

Section: Resultsmentioning

confidence: 99%

“…The aim of fitness partner recommendation is to match users of a system who are similar, to motivate them and help them maintain their PA. Of the 3 studies of this type, 2 studies [56,74] used recommender systems for finding a suitable partner. The other study [95] attempted to find a similar user by matching all users who crossed the particular user during a running activity.…”

Section: Resultsmentioning

confidence: 99%

“…Some others estimated it by the time spent at different PA levels, such as vigorous or MVPA [41,87]. There were studies which set the target HR and used specific HR monitors for data collection [80,81,95], whereas 1 used a smartwatch [75]. Step count was another measure used to evaluate PA, obtained directly from fitness tracking devices [2,3,9].…”

Section: Resultsmentioning

confidence: 99%

“…Demographics formed a part of the user model for 39 studies. Demographic data collected included age and gender [75], body mass index [7,32], employment [64,79], nationality [7], weight [79,95], marital status [47,88,97], and education [64,79]. Several studies collected demographic information but did not use it for providing the personalized intervention.…”

Section: Resultsmentioning

confidence: 99%

“…In our review, 4 papers used the social media profile to motivate users through activity status posts on websites or by inspiration from friends. Of these, 2 studies [32,95] used the profile to provide better recommendations and persuasion to users. On the other hand, the Active2Gether and PRO-Fit systems used social media in a direct way to generate social comparison [3] and recommend a fitness buddy to the user [56].…”

Section: Resultsmentioning

confidence: 99%

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User Models for Personalized Physical Activity Interventions: Scoping Review

Ghanvatkar¹,

Kankanhalli²,

Rajan³

2019

JMIR Mhealth Uhealth

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BackgroundFitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.ObjectiveThis review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.MethodsA scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.ResultsThe literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.ConclusionsThis review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of suc...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

User Models for Personalized Physical Activity Interventions: Scoping Review

Ghanvatkar¹,

Kankanhalli²,

Rajan³

2019

JMIR Mhealth Uhealth

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Competitive influences of running applications on training habits

Shei

2018

The Physician and Sportsmedicine

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The use of running applications (RAs) has recently been shown to neither increase nor decrease the risk of running-related injuries (RRIs). However, it is important to note that the study reporting these findings did not account for the influence of virtual competition or challenges, such as virtual leaderboards for certain popular "segments" and monthly "challenges" which reward users for completing predefined tasks in a given time period. Both of these factors may affect the training behaviors of RA users, and indeed may contribute to the risk of RRIs. In particular, the latter may mimic a predefined running scheme, which had a positive relationship with RRIs. Therefore, it is important for future studies to elucidate whether virtual competitions or challenges promoted by RAs influence running behavior, and consequently, the risk for RRIs.

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