Movement toward a novel activity monitoring device

Montgomery‐Downs, Hawley E.; Insana, Salvatore; Bond, Jonathan A.

doi:10.1007/s11325-011-0585-y

Cited by 281 publications

(220 citation statements)

References 4 publications

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“…23 It is possible that our findings reflect the user-friendly design of new wearable devices and corresponding smartphone applications. 24 At the same time, participants expressed concerns about affordability of these devices, highlighting broader concerns regarding the feasibility of using emerging m-health technologies in low-income patient populations.…”

Section: Discussionmentioning

confidence: 74%

Feasibility of Popular m-Health Technologies for Activity Tracking Among Individuals with Serious Mental Illness

Naslund

Aschbrenner

Barre

et al. 2015

Telemedicine and e-Health

144

101

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Obesity prevalence is nearly double among individuals with serious mental illness (SMI), including schizophrenia spectrum disorders, bipolar disorder, or major depressive disorder, compared with the general population. Emerging mobile health (m-health) technologies are increasingly available and offer the potential to support lifestyle interventions targeting weight loss, yet the practical feasibility of using these technologies in this high-risk group has not been established. We evaluated the feasibility and acceptability of popular m-health technologies for activity tracking among overweight and obese individuals with SMI. We provided wearable activity monitoring devices (FitBit [San Francisco, CA] ZipÔ or Nike Inc. [Beaverton, OR] FuelBand) and smartphones (Apple [Cupertino, CA] iPhone Ò 4S) for accessing the smartphone application for each device to participants with SMI enrolled in a weight loss program. Feasibility of these devices was measured by the frequency of use over time. Acceptability was measured through qualitative follow-up interviews with participants. Ten participants with SMI wore the devices for a mean of 89% (standard deviation = 13%) of the days in the study. Five participants wore the devices 100% of the time. Participants reported high satisfaction, stating the devices were easy to use, helpful for setting goals, motivational, and useful for selfmonitoring. Several participants liked the social connectivity feature of the devices where they could see each other's progress on the smartphone application, noting that ''friendly'' competition increased motivation to be more physically active. This study supports using popular m-health technologies for activity tracking among individuals with SMI. These findings can inform the design of weight loss interventions targeting this vulnerable patient population.

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Section: Discussionmentioning

confidence: 74%

Feasibility of Popular m-Health Technologies for Activity Tracking Among Individuals with Serious Mental Illness

Naslund

Aschbrenner

Barre

et al. 2015

Telemedicine and e-Health

144

101

View full text Add to dashboard Cite

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“…While several devices on the market have been shown to be reasonably accurate in specific populations and applications, 3 there are conflicting results from studies attempting to validate sleep trackers against gold standard polysomnography. 3 Some of the discrepancies likely relate to differences in the populations being studied, such as adolescents [4][5][6] versus adults, 7,8 and mixed groups with and without sleep disorders, [5][6][7] which is important because, similar to standard actigraphy, 9 commercial devices perform less well compared to PSG in persons with frequent awakenings and/or periods of immobile wakefulness. Discrepancies also arise through testing different models of devices.…”

mentioning

confidence: 99%

“…Ultra, Classic, FitbitChargeHR, compared with PSG, showed vastly different discrepancies when compared to PSG. 5,8,10 Adding to the confusion of whether or not commercial devices are valid is the attempt of some companies to measure time spent in "light" or "sound" sleep, or "dream" sleep, with little information provided how these sleep states are defined or measured. There is simply insufficient evidence to derive broad research and/or clinical uses at the present time.…”

mentioning

confidence: 99%

The Boom in Wearable Technology: Cause for Alarm or Just What is Needed to Better Understand Sleep?

Zambotti¹,

Godino²,

Baker³

et al. 2016

Sleep

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Wearable technology is ubiquitous in western society. Millions of low-cost, high-tech devices have been sold worldwide and consumers are using them to monitor multiple health-related signals, including sleep. [1][2][3] In clinical settings, patients presenting with various sleep disturbances increasingly show up with data from wearables on their smart phones. Clinicians are often befuddled when asked to interpret these data because there are no accepted guidelines or standards on how to interpret it, in part because the FDA does not regulate consumer-level wearables that provide "general wellness" information.3 Although the majority of the wearable technology industry claim that their products are not intended for scientific or medical purposes, researchers and clinicians focusing on sleep are waking up to the idea that these inexpensive tools could be used to monitor research participants and patients, and have begun using wearables in research and clinical practice. Given the widespread use and popularity of these devices, they could also be a new resource for healthcare providers to track a patient's behavior, such as sleep patterns, in relation to disease progression or treatment over a long period of time. Thus, wearables are not only consumer products but are also part of the health information technology revolution that aims to improve health information management, prevent medical errors and reduce health care costs.For wearable technology that purports to measure sleep to be accepted by the research and medical community, it needs to be vetted and have proven reliability and accuracy. While several devices on the market have been shown to be reasonably accurate in specific populations and applications, 3 there are conflicting results from studies attempting to validate sleep trackers against gold standard polysomnography.3 Some of the discrepancies likely relate to differences in the populations being studied, such as adolescents 4-6 versus adults, 7,8 and mixed groups with and without sleep disorders, 5-7 which is important because, similar to standard actigraphy, 9 commercial devices perform less well compared to PSG in persons with frequent awakenings and/or periods of immobile wakefulness. Discrepancies also arise through testing different models of devices. For example, studies evaluating different generation models of Fitbit (Fitbit, Inc., San Francisco, CA, USA), i.e. Ultra, Classic, FitbitChargeHR, compared with PSG, showed vastly different discrepancies when compared to PSG. 5,8,10 Adding to the confusion of whether or not commercial devices are valid is the attempt of some companies to measure time spent in "light" or "sound" sleep, or "dream" sleep, with little information provided how these sleep states are defined or measured. There is simply insufficient evidence to derive broad research and/or clinical uses at the present time.3 Clearly, more validation studies are needed both in the laboratory and at home. A more pressing need is pii: sp-00243-16 http://dx.doi.org/10.5665/sleep.6108for a...

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“…Following this initiative, for example, Quantified Self tools present problems regarding the accuracy of the self-measured values. For instance, Montgomery-Downs et al (2012) report a study on the validity and reliability of Fitbit, regarding activity monitoring in which they report an overestimation of sleep time and quality, owing to a misidentification of the waking pattern as sleep. Intensive validation is needed, if devices such as Fitbit are to be used for self-care, especially by populations with specific and critical health needs (Montgomery-Downs et al 2012).…”

Section: Challenges Of Self-monitoringmentioning

confidence: 99%

“…For instance, Montgomery-Downs et al (2012) report a study on the validity and reliability of Fitbit, regarding activity monitoring in which they report an overestimation of sleep time and quality, owing to a misidentification of the waking pattern as sleep. Intensive validation is needed, if devices such as Fitbit are to be used for self-care, especially by populations with specific and critical health needs (Montgomery-Downs et al 2012). Furthermore, the strong emphasis on quantification of health outcomes, such as achieving dietary or glucose-level goals, may hinder the real value of self-monitoring.…”

Section: Challenges Of Self-monitoringmentioning

confidence: 99%

On preventive blood pressure self-monitoring at home

Verdezoto

Grönvall

2015

Cogn Tech Work

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Self-monitoring activities are increasingly parts of people's everyday lives. Some of these measurements are performed voluntary rather than being referred by a physician, and conducted because of either a preventive health interest, or to better understand the body and its functions (so-called 'Quantified Self'). In this article, we explore socio-technical complexities that may occur when introducing preventive health-measurement technologies into older adults' daily routines and everyday lives. In particular, the original study investigated blood pressure (BP) measurement in nonclinical settings, to understand existing challenges, and uncover opportunities for self-monitoring technologies to support preventive healthcare activities among older adults. From our study, several aspects emerged that are important to consider when designing preventive self-monitoring technology, such as the complexity of guidelines for selfmeasuring, the importance of interpretation, understanding and health awareness, sharing self-monitoring information for prevention, various motivational factors, the role of the doctor in prevention, the home as a distributed information space, and so on. An awareness of these aspects may help designers to develop better support tools for people's preventive self-monitoring needs, compared to existing solutions. Supporting the active and informed individual may improve their self-care, awareness, and implementation of preventive care. Based on our study, we reflect on the findings to illustrate how these aspects may also inform both people engaged in Quantified Self activities and designers alike, and the tools and approaches that have sprung from the so-called 'Quantified Self' movement.

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Movement toward a novel activity monitoring device

Cited by 281 publications

References 4 publications

Feasibility of Popular m-Health Technologies for Activity Tracking Among Individuals with Serious Mental Illness

Feasibility of Popular m-Health Technologies for Activity Tracking Among Individuals with Serious Mental Illness

The Boom in Wearable Technology: Cause for Alarm or Just What is Needed to Better Understand Sleep?

On preventive blood pressure self-monitoring at home

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