The Digital Outcome Measure

Cohen, Adam B.; Mathews, Simon C.

doi:10.1159/000492396

Cited by 17 publications

(17 citation statements)

References 45 publications

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“…Beyond surgery, the concept of digital phenotyping has been extensively applied in the mental health arena for objective continuous generation of data points representing activities, cognitions, and behaviors (e.g., self-evaluated mood, daily steps, call durations, text frequency, psychosocial PROMs) in the management of a conditions including depression, anxiety, bipolar disease, schizophrenia and monitoring suicidal risk [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review

Jayakumar

Lin

Galea

et al. 2020

JPM

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Digital phenotyping—the moment-by-moment quantification of human phenotypes in situ using data related to activity, behavior, and communications, from personal digital devices, such as smart phones and wearables—has been gaining interest. Personalized health information captured within free-living settings using such technologies may better enable the application of patient-generated health data (PGHD) to provide patient-centered care. The primary objective of this scoping review is to characterize the application of digital phenotyping and digitally captured active and passive PGHD for outcome measurement in surgical care. Secondarily, we synthesize the body of evidence to define specific areas for further work. We performed a systematic search of four bibliographic databases using terms related to “digital phenotyping and PGHD,” “outcome measurement,” and “surgical care” with no date limits. We registered the study (Open Science Framework), followed strict inclusion/exclusion criteria, performed screening, extraction, and synthesis of results in line with the PRISMA Extension for Scoping Reviews. A total of 224 studies were included. Published studies have accelerated in the last 5 years, originating in 29 countries (mostly from the USA, n = 74, 33%), featuring original prospective work (n = 149, 66%). Studies spanned 14 specialties, most commonly orthopedic surgery (n = 129, 58%), and had a postoperative focus (n = 210, 94%). Most of the work involved research-grade wearables (n = 130, 58%), prioritizing the capture of activity (n = 165, 74%) and biometric data (n = 100, 45%), with a view to providing a tracking/monitoring function (n = 115, 51%) for the management of surgical patients. Opportunities exist for further work across surgical specialties involving smartphones, communications data, comparison with patient-reported outcome measures (PROMs), applications focusing on prediction of outcomes, monitoring, risk profiling, shared decision making, and surgical optimization. The rapidly evolving state of the art in digital phenotyping and capture of PGHD offers exciting prospects for outcome measurement in surgical care pending further work and consideration related to clinical care, technology, and implementation.

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Section: Discussionmentioning

confidence: 99%

Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review

Jayakumar

Lin

Galea

et al. 2020

JPM

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“…Employing phenotyping data collection by mobile technology in PCTs that depend less on active patient participation may lead to greater consistency, frequency and accuracy, and may be more amenable to standardization, with incremental potential for the advancement of precision medicine. 103 Neuroimaging Neuroimaging refers to a broad class of techniques, but here we focus on fMRI, which indirectly measures neuronal activity across the brain over time at high spatial resolution. Taskbased fMRI is a measurement of evoked brain activity in response to an external task, and resting-state fMRI measures correlations in brain activity between different brain areas while a person lies quietly at rest.…”

Section: Digital Phenotypingmentioning

confidence: 99%

Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders

Lenze

Nicol

Barbour

et al. 2021

jpn

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The goal of precision medicine (individually tailored treatments) is not being achieved for neurobehavioural conditions such as psychiatric disorders. Traditional randomized clinical trial methods are insufficient for advancing precision medicine because of the dynamic complexity of these conditions. We present a pragmatic solution: the precision clinical trial framework, encompassing methods for individually tailored treatments. This framework includes the following: (1) treatment-targeted enrichment, which involves measuring patients’ response after a brief bout of an intervention, and then randomizing patients to a full course of treatment, using the acute response to predict long-term outcomes; (2) adaptive treatments, which involve adjusting treatment parameters during the trial to individually optimize the treatment; and (3) precise measurement, which involves measuring predictor and outcome variables with high accuracy and reliability using techniques such as ecological momentary assessment. This review summarizes precision clinical trials and provides a research agenda, including new biomarkers such as precision neuroimaging, transcranial magnetic stimulation–electroencephalogram digital phenotyping and advances in statistical and machine-learning models. Validation of these approaches — and then widespread incorporation of the precision clinical trial framework — could help achieve the vision of precision medicine for neurobehavioural conditions.

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“…The International Classification of Functioning, Disability and Health was introduced by the World Health Organization to differentiate between assessments in a standardized-environment that measure motor capacity, which indicates the highest possible level of function at a specific moment, versus real-life assessments that measure mobility performance, which reflects what individuals do in their natural environment [17]. Digital biomarkers are objective, quantifiable physiological and behavioral data collected and measured by digital devices such as wearable sensors, and applied to the advancement of health [18]. Digital biomarkers of mobility performance may provide a more reliable estimate of physical activity, which is important for clinical decision-making and evaluation of treatment-related adverse effects [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…The rise of digital health technology, such as fitness and activity trackers, allows monitoring of mobility performance metrics in real-world conditions at a more granular level and can display changes over time. However, the optimal integration of digital biomarkers in clinical decision making remains unknown [18]. There has been an increase in the application of digital biomarkers for cancer care in all phases of cancer survivorship, such as assessment of quality of life, performance status, on-treatment toxicity, and long-term changes in mobility performance after treatment [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Harnessing digital health to objectively assess cancer-related fatigue: The impact of fatigue on mobility performance

et al. 2021

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Objective Cancer-related fatigue (CRF) is highly prevalent among cancer survivors, which may have long-term effects on physical activity and quality of life. CRF is assessed by self-report or clinical observation, which may limit timely diagnosis and management. In this study, we examined the effect of CRF on mobility performance measured by a wearable pendant sensor. Methods This is a secondary analysis of a clinical trial evaluating the benefit of exercise in cancer survivors with chemotherapy-induced peripheral neuropathy (CIPN). CRF status was classified based on a Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) score ≤ 33. Among 28 patients (age = 65.7±9.8 years old, BMI = 26.9±4.1kg/m2, sex = 32.9%female) with database variables of interest, twenty-one subjects (75.9%) were classified as non-CRF. Mobility performance, including behavior (sedentary, light, and moderate to vigorous activity (MtV)), postures (sitting, standing, lying, and walking), and locomotion (e.g., steps, postural transitions) were measured using a validated pendant-sensor over 24-hours. Baseline psychosocial, Functional Assessment of Cancer Therapy–General (FACT-G), Falls Efficacy Scale–International (FES-I), and motor-capacity assessments including gait (habitual speed, fast speed, and dual-task speed) and static balance were also performed. Results Both groups had similar baseline clinical and psychosocial characteristics, except for body-mass index (BMI), FACT-G, FACIT-F, and FES-I (p<0.050). The groups did not differ on motor-capacity. However, the majority of mobility performance parameters were different between groups with large to very large effect size, Cohen’s d ranging from 0.91 to 1.59. Among assessed mobility performance, the largest effect sizes were observed for sedentary-behavior (d = 1.59, p = 0.006), light-activity (d = 1.48, p = 0.009), and duration of sitting+lying (d = 1.46, p = 0.016). The largest correlations between mobility performance and FACIT-F were observed for sitting+lying (rho = -0.67, p<0.001) and the number of steps per day (rho = 0.60, p = 0.001). Conclusion The results of this study suggest that sensor-based mobility performance monitoring could be considered as a potential digital biomarker for CRF assessment. Future studies warrant evaluating utilization of mobility performance to track changes in CRF over time, response to CRF-related interventions, and earlier detection of CRF.

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The Digital Outcome Measure

Cited by 17 publications

References 45 publications

Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review

Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review

Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders

Harnessing digital health to objectively assess cancer-related fatigue: The impact of fatigue on mobility performance

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