Explaining Changes in Physical Activity Through a Computational Model of Social Contagion

Mollee, Julia S.; Araújo, Eric F. M.; Manzoor, Alina; Halteren, Aart van; Klein, Michel

doi:10.1007/978-3-319-54241-6_19

Cited by 3 publications

(8 citation statements)

References 19 publications

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“…Other studies created windows or segments of time to calculate PA characteristics, including segments of steps or sleep [ 15 ] and PA bouts [ 13 , 24 ]. Other articles used the preprocessed data to calculate the participants’ step achievements such as whether they reached their step goal [ 9 , 11 , 19 , 23 ]. Zhu et al [ 13 ] used more complex features such as the ratio between the most active and least active period or the circadian rhythm strength.…”

Section: Resultsmentioning

confidence: 99%

“…These algorithms analyze the psychosocial influences on the participants’ PA. For example, Mollee et al [ 23 ] analyzed the PA dynamics in a community using a social contagion model. Mollee and Klein [ 25 ] analyzed the PA dynamics in a networked community using social cognitive theories, and Zhu et al [ 14 ] personalized social comparison during an intervention to increase the participants’ PA.…”

Section: Resultsmentioning

confidence: 99%

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Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

Díaz¹,

Caillaud²,

Yacef³

2023

JMIR Med Inform

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Background Sensors are increasingly used in health interventions to unobtrusively and continuously capture participants’ physical activity in free-living conditions. The rich granularity of sensor data offers great potential for analyzing patterns and changes in physical activity behaviors. The use of specialized machine learning and data mining techniques to detect, extract, and analyze these patterns has increased, helping to better understand how participants’ physical activity evolves. Objective The aim of this systematic review was to identify and present the various data mining techniques employed to analyze changes in physical activity behaviors from sensors-derived data in health education and health promotion intervention studies. We addressed two main research questions: (1) What are the current techniques used for mining physical activity sensor data to detect behavior changes in health education or health promotion contexts? (2) What are the challenges and opportunities in mining physical activity sensor data for detecting physical activity behavior changes? Methods The systematic review was performed in May 2021 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We queried the Association for Computing Machinery (ACM), IEEE Xplore, ProQuest, Scopus, Web of Science, Education Resources Information Center (ERIC), and Springer literature databases for peer-reviewed references related to wearable machine learning to detect physical activity changes in health education. A total of 4388 references were initially retrieved from the databases. After removing duplicates and screening titles and abstracts, 285 references were subjected to full-text review, resulting in 19 articles included for analysis. Results All studies used accelerometers, sometimes in combination with another sensor (37%). Data were collected over a period ranging from 4 days to 1 year (median 10 weeks) from a cohort size ranging between 10 and 11615 (median 74). Data preprocessing was mainly carried out using proprietary software, generally resulting in step counts and time spent in physical activity aggregated predominantly at the daily or minute level. The main features used as input for the data mining models were descriptive statistics of the preprocessed data. The most common data mining methods were classifiers, clusters, and decision-making algorithms, and these focused on personalization (58%) and analysis of physical activity behaviors (42%). Conclusions Mining sensor data offers great opportunities to analyze physical activity behavior changes, build models to better detect and interpret behavior changes, and allow for personalized feedback and support for participants, especially where larger sample sizes and longer recording times are available. Exploring different data aggregation levels can help detect subtle and sustained behavior changes. However, the literature suggests that there is still work remaining to improve the transparency, explicitness, and standardization of the data preprocessing and mining processes to establish best practices and make the detection methods easier to understand, scrutinize, and reproduce.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

Díaz¹,

Caillaud²,

Yacef³

2023

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…Ref. [ 16 ] explained the PAL dynamics in a community using a social contagion model to model the steps and results of a psychological questionnaire on self-efficacy, barriers, social norms, long-term goals, intentions, satisfaction, outcome expectations, and models. Ref.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data

Díaz

Caillaud

Yacef

2022

Sensors

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Wearable accelerometers record physical activity with high resolution, potentially capturing the rich details of behaviour changes and habits. Detecting these changes as they emerge is valuable information for any strategy that promotes physical activity and teaches healthy behaviours or habits. Indeed, this offers the opportunity to provide timely feedback and to tailor programmes to each participant’s needs, thus helping to promote the adherence to and the effectiveness of the intervention. This article presents and illustrates U-BEHAVED, an unsupervised algorithm that periodically scans step data streamed from activity trackers to detect physical activity behaviour changes to assess whether they may become habitual patterns. Using rolling time windows, current behaviours are compared with recent previous ones, identifying any significant change. If sustained over time, these new behaviours are classified as potentially new habits. We validated this detection algorithm using a physical activity tracker step dataset (N = 12,798) from 79 users. The algorithm detected 80% of behaviour changes of at least 400 steps within the same hour in users with low variability in physical activity, and of 1600 steps in those with high variability. Based on a threshold cadence of approximately 100 steps per minute for standard walking pace, this number of steps would suggest approximately 4 and 16 min of physical activity at moderate-to-vigorous intensity, respectively. The detection rate for new habits was 80% with a minimum threshold of 500 or 1600 steps within the same hour in users with low or high variability, respectively.

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“…Chen et al [21] Applied Sciences Dijkhuis et al [22] Sensors Mollee et al [23] Springer Proceedings in Complexity Diaz et al [24] IEEE Access Mollee and Klein [25] International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems…”

Section: Engineering and Sciencementioning

confidence: 99%

“…Resulting/initial preprocessing [7,9,[13][14][15]17,19,22,25] Steps: unknown (proprietary program) [21] Metabolic equivalents: unknown (proprietary program) [10,17,19] Calories: unknown (proprietary program) [10,17,21] Exercise characteristics: unknown (proprietary program) a [15,17] Sleeping time: unknown (proprietary program) [15,19] Weight: unknown (proprietary program) [17,21] Heart rate: unknown (proprietary program) Physical activity (PA) levels [11,24] Signal vector magnitudes [8] PA counts [12] Metabolic equivalents [18] Activity classes [23] Not specified [20] Integrals of the moduli of acceleration signals [16] Actual activity level; not specified b a Type, duration, distance, frequency.…”

Section: Referencementioning

confidence: 99%

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review (Preprint)

Díaz¹,

Caillaud²,

Yacef³

2022

Preprint

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BACKGROUND Sensors are increasingly used in health interventions to unobtrusively and continuously capture participants’ physical activity in free-living conditions. The rich granularity of sensor data offers great potential for analyzing patterns and changes in physical activity behaviors. The use of specialized machine learning and data mining techniques to detect, extract, and analyze these patterns has increased, helping to better understand how participants’ physical activity evolves. OBJECTIVE The aim of this systematic review was to identify and present the various data mining techniques employed to analyze changes in physical activity behaviors from sensors-derived data in health education and health promotion intervention studies. We addressed two main research questions: (1) What are the current techniques used for mining physical activity sensor data to detect behavior changes in health education or health promotion contexts? (2) What are the challenges and opportunities in mining physical activity sensor data for detecting physical activity behavior changes? METHODS The systematic review was performed in May 2021 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We queried the Association for Computing Machinery (ACM), IEEE Xplore, ProQuest, Scopus, Web of Science, Education Resources Information Center (ERIC), and Springer literature databases for peer-reviewed references related to wearable machine learning to detect physical activity changes in health education. A total of 4388 references were initially retrieved from the databases. After removing duplicates and screening titles and abstracts, 285 references were subjected to full-text review, resulting in 19 articles included for analysis. RESULTS All studies used accelerometers, sometimes in combination with another sensor (37%). Data were collected over a period ranging from 4 days to 1 year (median 10 weeks) from a cohort size ranging between 10 and 11615 (median 74). Data preprocessing was mainly carried out using proprietary software, generally resulting in step counts and time spent in physical activity aggregated predominantly at the daily or minute level. The main features used as input for the data mining models were descriptive statistics of the preprocessed data. The most common data mining methods were classifiers, clusters, and decision-making algorithms, and these focused on personalization (58%) and analysis of physical activity behaviors (42%). CONCLUSIONS Mining sensor data offers great opportunities to analyze physical activity behavior changes, build models to better detect and interpret behavior changes, and allow for personalized feedback and support for participants, especially where larger sample sizes and longer recording times are available. Exploring different data aggregation levels can help detect subtle and sustained behavior changes. However, the literature suggests that there is still work remaining to improve the transparency, explicitness, and standardization of the data preprocessing and mining processes to establish best practices and make the detection methods easier to understand, scrutinize, and reproduce.

show abstract

Explaining Changes in Physical Activity Through a Computational Model of Social Contagion

Cited by 3 publications

References 19 publications

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review (Preprint)

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