Evidence map›Paper›PMID 30950807›Full record

ArticleJMIR mHealth and uHealth2019

The Use of Wearable Activity Trackers Among Older Adults: Focus Group Study of Tracker Perceptions, Motivators, and Barriers in the Maintenance Stage of Behavior Change.

Anastasia Kononova, Lin Li, Kendra Kamp, Marie Bowen, R V Rikard, Shelia Cotten, Wei Peng

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
103citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

103 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  16. Night Watch: A Survey of Older Adult Sleep-Tracking.Behavioral sciences (Basel, Switzerland) · 2026
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  18. Observational
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43 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Anastasia KononovaDepartment of Advertising and Public Relations, Michigan State University, East Lansing, MI, United States.ORCID 0000-0001-6636-0357
Lin LiDepartment of Media and Information, Michigan State University, East Lansing, MI, United States.ORCID 0000-0002-8197-9155
Kendra KampDepartment of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle, WA, United States.ORCID 0000-0002-7753-3564
Marie BowenCenter for Innovation and Research, Michigan State University, East Lansing, MI, United States.ORCID 0000-0003-3658-301X
R V RikardDepartment of Media and Information, Michigan State University, East Lansing, MI, United States.ORCID 0000-0003-2201-553X
Shelia CottenDepartment of Media and Information, Michigan State University, East Lansing, MI, United States.ORCID 0000-0002-8657-8262
Wei PengDepartment of Media and Information, Michigan State University, East Lansing, MI, United States.ORCID 0000-0003-1576-4532

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWearable activity trackers offer the opportunity to increase physical activity through continuous monitoring. Viewing tracker use as a beneficial health behavior, we explored the factors that facilitate and hinder long-term activity tracker use, applying the transtheoretical model of behavior change with the focus on the maintenance stage and relapse.

objectiveThe aim of this study was to investigate older adults' perceptions and uses of activity trackers at different points of use: from nonuse and short-term use to long-term use and abandoned use to determine the factors to maintain tracker use and prevent users from discontinuing tracker usage.

methodsData for the research come from 10 focus groups. Of them, 4 focus groups included participants who had never used activity trackers (n=17). These focus groups included an activity tracker trial. The other 6 focus groups (without the activity tracker trial) were conducted with short-term (n=9), long-term (n=11), and former tracker users (n=11; 2 focus groups per user type).

resultsThe results revealed that older adults in different tracker use stages liked and wished for different tracker features, with long-term users (users in the maintenance stage) being the most diverse and sophisticated users of the technology. Long-term users had developed a habit of tracker use whereas other participants made an effort to employ various encouragement strategies to ensure behavior maintenance. Social support through collaboration was the primary motivator for long-term users to maintain activity tracker use. Short-term and former users focused on competition, and nonusers engaged in vicarious tracker use experiences. Former users, or those who relapsed by abandoning their trackers, indicated that activity tracker use was fueled by curiosity in quantifying daily physical activity rather than the desire to increase physical activity. Long-term users saw a greater range of pros in activity tracker use whereas others focused on the cons of this behavior.

conclusionsThe results suggest that activity trackers may be an effective technology to encourage physical activity among older adults, especially those who have never tried it. However, initial positive response to tracker use does not guarantee tracker use maintenance. Maintenance depends on recognizing the long-term benefits of tracker use, social support, and internal motivation. Nonadoption and relapse may occur because of technology's limitations and gaining awareness of one's physical activity without changing the physical activity level itself.

Indexed as

PerceptionAgedAged, 80 and overBehavior TherapyFemaleFitness TrackersFocus GroupsHumansMaleMotivationQualitative ResearchWearable Electronic Devicesagingbiobehavioral sciencesexercisephysical activitytranstheoretical model of behavior changewearable electronic devices

Identifiers

PMID30950807
PMCPMC6473213

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.