Activity trackers implement different behavior change techniques for activity, sleep, and sedentary behaviors

Duncan, Mitch; Murawski, Beatrice; Short, Camille E; Rebar, Amanda L; Schoeppe, Stephanie; Alley, Stephanie; Vandelanotte, Corneel; Kirwan, Morwenna

doi:10.2196/ijmr.6685

Cited by 69Mobile Health and mHealth ApplicationsInnovative Human-Technology InteractionPhysical Activity and Health

Activity trackers implement different behavior change techniques for activity, sleep, and sedentary behaviors

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Duncan, Mitch, Murawski, Beatrice, Short, Camille E, Rebar, Amanda L, Schoeppe, Stephanie, Alley, Stephanie, Vandelanotte, Corneel, Kirwan, Morwenna·2017·Interactive Journal of Medical Research

DOI 10.2196/ijmr.6685PMC PMC5575434

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Abstract

BACKGROUND: Several studies have examined how the implementation of behavior change techniques (BCTs) varies between different activity trackers. However, activity trackers frequently allow tracking of activity, sleep, and sedentary behaviors; yet, it is unknown how the implementation of BCTs differs between these behaviors. OBJECTIVE: The aim of this study was to assess the number and type of BCTs that are implemented by wearable activity trackers (self-monitoring systems) in relation to activity, sleep, and sedentary behaviors and to determine whether the number and type of BCTs differ between behaviors. METHODS: Three self-monitoring systems (Fitbit [Charge HR], Garmin [Vivosmart], and Jawbone [UP3]) were each used for a 1-week period in August 2015. Each self-monitoring system was used by two of the authors (MJD and BM) concurrently. The Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy was used to assess the implementation of 40 BCTs in relation to activity, sleep, and sedentary behaviors. Discrepancies in ratings were resolved by discussion, and interrater agreement in the number of BCTs implemented was assessed using kappa statistics. RESULTS: Interrater agreement ranged from 0.64 to 1.00. From a possible range of 40 BCTs, the number of BCTs present for activity ranged from 19 (Garmin) to 33 (Jawbone), from 4 (Garmin) to 29 (Jawbone) for sleep, and 0 (Fitbit) to 10 (Garmin) for sedentary behavior. The average number of BCTs implemented was greatest for activity (n=26) and smaller for sleep (n=14) and sedentary behavior (n=6). CONCLUSIONS: The number and type of BCTs implemented varied between each of the systems and between activity, sleep, and sedentary behaviors. This provides an indication of the potential of these systems to change these behaviors, but the long-term effectiveness of these systems to change activity, sleep, and sedentary behaviors remains unknown.

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Smartwatches often emit insufficient light for accurate readings on darker-skinned users, leading to inconsistent results and potential misdiagnosis.
“Studies show that smartwatches often emit insufficient light for accurate readings on darker-skinned users, leading to inconsistent results and potential misdiagnosis.”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [22]
The Methods section (page unavailable) describes an assessment of behavior-change techniques in three wrist-worn activity-tracker systems. The Discussion mentions a limitation in how those systems classify sedentary behavior, but our copy does not address emitted light, skin tone, reading accuracy, or misdiagnosis (searched for darker skin, light, optical sensors, and accuracy). (LLM, conf=0.78)
Experimental — accuracy not yet measured
These discrepancies undermine trust in wearable health devices, especially when inaccurate readings affect fitness tracking or miss critical signs like arrhythmias.
“These discrepancies undermine trust in wearable health devices, especially when inaccurate readings affect fitness tracking or miss critical signs like arrhythmias [21,22].”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [22]
The Methods describe assessing behavior-change techniques in activity trackers, and the Discussion mentions technical limitations in how the trackers classify sedentary behavior. Neither section says that inaccurate readings undermine users’ trust or cause devices to miss arrhythmias (searched for accuracy, trust, and arrhythmia). (LLM, conf=0.76)
Experimental — accuracy not yet measured
Socioeconomic disparities in access to smartwatches may interact with skin-tone-related bias, raising additional equity concerns.
“However, regional variations may exist, and socioeconomic disparities in access to these devices may further interact with skin-tone-related bias, raising additional equity concerns [17-23].”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [22]
The Introduction reports overall device ownership, and the Methods describe an assessment of behavior-change features in three wrist-worn trackers; neither addresses socioeconomic differences in access. Searches for skin-tone bias, affordability, and equity found no passage discussing their proposed interaction. Our copy therefore does not verify the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured

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Authors: Duncan, M., Murawski, B., Short, C. E., Rebar, A. L., Schoeppe, S., Alley, S., Vandelanotte, C., & Kirwan, M.
Affiliations: University of Newcastle Australia (AU)
Freemasons Foundation Centre for Men's Health (AU)
Central Queensland University (AU)
Western Sydney University (AU)
Corresponding author: Duncan, Mitch
ORCID: Duncan, Mitch
Murawski, Beatrice
Short, Camille E
Rebar, Amanda L
Schoeppe, Stephanie
Alley, Stephanie
Vandelanotte, Corneel
Kirwan, Morwenna
Venue: Interactive Journal of Medical Research
Publisher: JMIR Publications Inc.
Published: 2017-08-14
Volume: 6
Issue: 2
Pages: e13
ISSN: 1929-073X (ISSN-L)
DOI: 10.2196/ijmr.6685
PMC: PMC5575434
OpenAlex: W2746003952
Cited by: 68
References: 48
Citations by year: 2026: 4 · 2025: 5 · 2024: 11 · 2023: 6 · 2022: 8 · 2021: 11 · 2020: 5 · 2019: 11 · 2018: 6 · 2017: 1
FWCI: 10.20
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: Interactive Journal of Medical Research (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Acquire (CQUniversity) (submittedVersion, cc-by)
Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by)
eSpace (Curtin University) (submittedVersion)
Figshare (submittedVersion, cc-by)
NOVA (University of Newcastle Australia) (submittedVersion, cc-by)
NOVA (University of Newcastle Australia) (submittedVersion, other-oa)
Topics: Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Health Sciences › Medicine › Physiology › Physical Activity and Health
Keywords: wearable activity trackers, behaviour change techniques, self-monitoring systems, physical activity, sleep behavior, sedentary behavior, Fitbit, Garmin, jaw bone, interobserver agreement

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Activity trackers implement different behavior change techniques for activity, sleep, and sedentary behaviors

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