Monitoring Daily Function in Persons With Transfemoral Amputations Using a Commercial Activity Monitor: A Feasibility Study

Albert, Mark V.; Deeny, Sean P.; McCarthy, C; Valentin, Juliana; Jayaraman, Arun

doi:10.1016/j.pmrj.2014.06.006

Cited by 25 publications

(27 citation statements)

References 16 publications

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“…Recently, there has been a growth in consumer-based personal fitness trackers that provide measurements of daily activity (i.e., steps) and sleep quality (Voets, 2013). While these products have been marketed toward healthy populations, there is growing interest in using these in clinical settings (Albert et al, 2014; Cook et al, 2013) and with bipolar patients, in particular (Puiatti et al, 2011). Findings are also consistent with tenets of Interpersonal and Social Rhythms Therapy (IPSRT), that train regularization of social activity and sleep timing (Frank et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

Clinical significance of mobile health assessed sleep duration and variability in bipolar disorder

Kaufmann

Gershon

Eyler

et al. 2016

Journal of Psychiatric Research

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OBJECTIVE Sleep disturbances are prevalent, persistent, and impairing features of bipolar disorder. However, the near-term and cumulative impact of the severity and variability of sleep disturbances on symptoms and functioning remains unclear. We examined self-reported daily sleep duration and variability in relation to mood symptoms, medication adherence, cognitive functioning, and concurrent daily affect. METHODS Forty-one outpatients diagnosed with bipolar disorder were asked to provide daily reports of sleep duration and affect collected via ecological momentary assessment with smartphones over eleven weeks. Measures of depressive and manic symptoms, medication adherence, and cognitive function were collected at baseline and concurrent assessment of affect were collected daily. Analyses examined whether sleep duration or variability were associated with baseline measures and changes in same-day or next-day affect. RESULTS Greater sleep duration variability (but not average sleep duration) was associated with greater depressive and manic symptom severity, and lower medication adherence at baseline, and with lower and more variable ratings of positive affect and higher ratings of negative affect. Sleep durations shorter than 7-8 hours were associated with lower same-day ratings of positive and higher same-day ratings of negative affect, however this did not extend to next-day affect. CONCLUSIONS Greater cumulative day-to-day sleep duration variability, but not average sleep duration, was related to more severe mood symptoms, lower self-reported medication adherence and higher levels of negative affect. Bouts of short- or long-duration sleep had transient impact on affect. Day-to-day sleep variability may be important to incorporate into clinical assessment of sleep disturbances in bipolar disorder.

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Section: Discussionmentioning

confidence: 99%

Clinical significance of mobile health assessed sleep duration and variability in bipolar disorder

Kaufmann

Gershon

Eyler

et al. 2016

Journal of Psychiatric Research

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“…We excluded abstracts (examples [ 15 , 16 ]) and conference proceedings (example [ 17 ]). We also excluded studies focused on special populations, such as stroke and traumatic brain injury [ 18 ], chronic obstructive pulmonary disease [ 19 ], amputation [ 20 ], mental illness [ 21 ], or older adults in assisted living [ 22 ]. One study presented data on apparently healthy older adults without mobility impairments and those of similar ages with reduced mobility; therefore, we reported only on those without mobility impairments [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Systematic review of the validity and reliability of consumer-wearable activity trackers

Evenson

Goto

Furberg

2015

Int J Behav Nutr Phys Act

1,046

802

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BackgroundConsumer-wearable activity trackers are electronic devices used for monitoring fitness- and other health-related metrics. The purpose of this systematic review was to summarize the evidence for validity and reliability of popular consumer-wearable activity trackers (Fitbit and Jawbone) and their ability to estimate steps, distance, physical activity, energy expenditure, and sleep.MethodsSearches included only full-length English language studies published in PubMed, Embase, SPORTDiscus, and Google Scholar through July 31, 2015. Two people reviewed and abstracted each included study.ResultsIn total, 22 studies were included in the review (20 on adults, 2 on youth). For laboratory-based studies using step counting or accelerometer steps, the correlation with tracker-assessed steps was high for both Fitbit and Jawbone (Pearson or intraclass correlation coefficients (CC) > =0.80). Only one study assessed distance for the Fitbit, finding an over-estimate at slower speeds and under-estimate at faster speeds. Two field-based studies compared accelerometry-assessed physical activity to the trackers, with one study finding higher correlation (Spearman CC 0.86, Fitbit) while another study found a wide range in correlation (intraclass CC 0.36–0.70, Fitbit and Jawbone). Using several different comparison measures (indirect and direct calorimetry, accelerometry, self-report), energy expenditure was more often under-estimated by either tracker. Total sleep time and sleep efficiency were over-estimated and wake after sleep onset was under-estimated comparing metrics from polysomnography to either tracker using a normal mode setting. No studies of intradevice reliability were found. Interdevice reliability was reported on seven studies using the Fitbit, but none for the Jawbone. Walking- and running-based Fitbit trials indicated consistently high interdevice reliability for steps (Pearson and intraclass CC 0.76–1.00), distance (intraclass CC 0.90–0.99), and energy expenditure (Pearson and intraclass CC 0.71–0.97). When wearing two Fitbits while sleeping, consistency between the devices was high.ConclusionThis systematic review indicated higher validity of steps, few studies on distance and physical activity, and lower validity for energy expenditure and sleep. The evidence reviewed indicated high interdevice reliability for steps, distance, energy expenditure, and sleep for certain Fitbit models. As new activity trackers and features are introduced to the market, documentation of the measurement properties can guide their use in research settings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12966-015-0314-1) contains supplementary material, which is available to authorized users.

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“…Indeed, an increasing number of studies apply machine learning to the data collected from these devices for clinical prediction purposes. Examples include detecting cardiovascular diseases [7], falls [8], measuring rehabilitation outcomes in stroke and amputees [9–11], monitoring Parkinson's disease (PD) symptoms [12–14], and detecting depression [15, 16]. …”

Section: Introductionmentioning

confidence: 99%

The need to approximate the use-case in clinical machine learning

et al. 2017

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The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map those data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is vital to reliably quantify their prediction accuracy. Cross-validation (CV) is the standard approach where the accuracy of such algorithms is evaluated on part of the data the algorithm has not seen during training. However, for this procedure to be meaningful, the relationship between the training and the validation set should mimic the relationship between the training set and the dataset expected for the clinical use. Here we compared two popular CV methods: record-wise and subject-wise. While the subject-wise method mirrors the clinically relevant use-case scenario of diagnosis in newly recruited subjects, the record-wise strategy has no such interpretation. Using both a publicly available dataset and a simulation, we found that record-wise CV often massively overestimates the prediction accuracy of the algorithms. We also conducted a systematic review of the relevant literature, and found that this overly optimistic method was used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning-based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as inaccurate results can mislead both clinicians and data scientists.

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Monitoring Daily Function in Persons With Transfemoral Amputations Using a Commercial Activity Monitor: A Feasibility Study

Cited by 25 publications

References 16 publications

Clinical significance of mobile health assessed sleep duration and variability in bipolar disorder

Clinical significance of mobile health assessed sleep duration and variability in bipolar disorder

Systematic review of the validity and reliability of consumer-wearable activity trackers

The need to approximate the use-case in clinical machine learning

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