A Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study

Background: Older adults who engage in physical activity can reduce their risk of mobility impairment and disability. Short amounts of walking can improve quality of life, physical function, and cardiovascular health. Various programs have been implemented to encourage older adults to engage in physical activity, but sustaining their motivation continues to be a challenge. Ubiquitous devices, such as mobile phones and smartwatches, coupled with machine-learning algorithms, can potentially encourage older adults to be more physically active. Current algorithms that are deployed in consumer devices (eg, Fitbit) are proprietary, often are not tailored to the movements of older adults, and have been shown to be inaccurate in clinical settings. Step-counting algorithms have been developed for smartwatches, but only using data from younger adults and, often, were only validated in controlled laboratory settings. Objective: We sought to develop and validate a smartwatch step-counting app for older adults and evaluate the algorithm in free-living settings over a long period of time. Methods: We developed and evaluated a step-counting app for older adults on an open-source wrist-worn device (Amulet). The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm in the lab (counting steps from a video recording, n=20) and in free-living conditions—one 2-day field study (n=6) and two 12-week field studies (using the Fitbit as ground truth, n=16). During app system development, we evaluated 4 walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field studies, we evaluated 5 different cut-off values for the algorithm, using correlation and error rate as the evaluation metrics. Results: The step-counting algorithm performed well. In the lab study, for normal walking (R2=0.5), there was a stronger correlation between the Amulet steps and the video-validated steps; for all activities, the Amulet’s count was on average 3.2 (2.1%) steps lower (SD 25.9) than the video-validated count. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2=0.989) and 3.1% (SD 25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 value of 0.669. Conclusions: Our findings demonstrate the importance of an iterative process in algorithm development before field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step counter). Our app could potentially be used to help improve physical activity among older adults

A Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study

Background: Older adults who engage in physical activity can reduce their risk of mobility impairment and disability. Short amounts of walking can improve quality of life, physical function, and cardiovascular health. Various programs have been implemented to encourage older adults to engage in physical activity, but sustaining their motivation continues to be a challenge. Ubiquitous devices, such as mobile phones and smartwatches, coupled with machine-learning algorithms, can potentially encourage older adults to be more physically active. Current algorithms that are deployed in consumer devices (eg, Fitbit) are proprietary, often are not tailored to the movements of older adults, and have been shown to be inaccurate in clinical settings. Step-counting algorithms have been developed for smartwatches, but only using data from younger adults and, often, were only validated in controlled laboratory settings. Objective: We sought to develop and validate a smartwatch step-counting app for older adults and evaluate the algorithm in free-living settings over a long period of time. Methods: We developed and evaluated a step-counting app for older adults on an open-source wrist-worn device (Amulet). The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm in the lab (counting steps from a video recording, n=20) and in free-living conditions—one 2-day field study (n=6) and two 12-week field studies (using the Fitbit as ground truth, n=16). During app system development, we evaluated 4 walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field studies, we evaluated 5 different cut-off values for the algorithm, using correlation and error rate as the evaluation metrics. Results: The step-counting algorithm performed well. In the lab study, for normal walking (R2=0.5), there was a stronger correlation between the Amulet steps and the video-validated steps; for all activities, the Amulet’s count was on average 3.2 (2.1%) steps lower (SD 25.9) than the video-validated count. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2=0.989) and 3.1% (SD 25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 value of 0.669. Conclusions: Our findings demonstrate the importance of an iterative process in algorithm development before field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step counter). Our app could potentially be used to help improve physical activity among older adults.ISSN:2561-760

Use of a Wearable Activity Device in Rural Older Obese Adults

Objective: Assess the feasibility and acceptability of Fitbit for supporting behavioral change in rural, older adults with obesity. Method: Eight adults aged ≥65 with a body mass index (BMI) ≥30kg/m 2 were recruited from a rural practice and provided a Fitbit Zip device for 30 days. Participants completed validated questionnaires/interviews. Results: Mean age was 73.4 ± 4.0 years (50% female) with a mean BMI of 34.5 ± 4.5kg/m 2 . We observed reductions in exercise confidence (sticking to it: 34.5 ± 3.3 to 30.9 ± 4.3, p = .04; making time: 18.9 ± 1.3 to 17.0 ± 2.6, p = .03) but no changes in patient activation (45.4 ± 4.3 vs. 45.0 ± 3.9). All reported high satisfaction, seven (87.5%) found Fitbit easy to use, and five (62.5%) found the feedback useful. The majority ( n = 6 [75.0%]) were mostly/very satisfied with the intervention. Consistent themes emerged regarding the benefit of self-monitoring and participant motivation. Common concerns included finding time to exercise and lack of a peer group. Conclusion: Use of Fitbit is feasible/acceptable for use among older rural obese adults but may lead to reduced confidence

Effectiveness of Ambulatory Telemedicine Care in Older Adults: A Systematic Review

BACKGROUND: Disparities in healthcare access and delivery, caused by transportation and health workforce difficulties, negatively impact individuals living in rural areas. These challenges are especially prominent in older adults. DESIGN: We systematically evaluated the feasibility, acceptability, and effectiveness in providing telemedicine (TMed), searching the English‐language literature for studies (January 2012 to July 2018) in the following databases: Medline (PubMed); Cochrane Library (Wiley); Web of Science; CINAHL; EMBASE (Ovid); and PsycINFO (EBSCO). PARTICIPANTS: Older adults (mean age = 65 years or older, and none were younger than 60 years). INTERVENTIONS: Interventions consisted of live, synchronous, two‐way videoconferencing communication in nonhospital settings. All medical interventions were included. MEASUREMENTS: Quality assessment, using the Cochrane Collaboration\u27s Risk‐of‐Bias Tool, was applied on all included articles, including a qualitative summary of all articles. RESULTS: Of 6,616 citations, we reviewed the full text of 1173 articles, excluding 1047 that did not meet criteria. Of the 17 randomized controlled trials, the United States was the country with the most trials (6 [35%]), with cohort sizes ranging from 3 to 844 (median = 35) participants. Risk of bias among included studies varied from low to high. Our qualitative analysis suggests that TMed can improve health outcomes in older adults and that it could be used in this population. CONCLUSIONS: TMed is feasible and acceptable in delivering care to older adults. Research should focus on well‐designed randomized trials to overcome the high degree of bias observed in our synthesis. Clinicians should consider using TMed in routine practice to overcome barriers of distance and access to care

Empagliflozin in Patients with Chronic Kidney Disease

Background The effects of empagliflozin in patients with chronic kidney disease who are at risk for disease progression are not well understood. The EMPA-KIDNEY trial was designed to assess the effects of treatment with empagliflozin in a broad range of such patients. Methods We enrolled patients with chronic kidney disease who had an estimated glomerular filtration rate (eGFR) of at least 20 but less than 45 ml per minute per 1.73 m(2) of body-surface area, or who had an eGFR of at least 45 but less than 90 ml per minute per 1.73 m(2) with a urinary albumin-to-creatinine ratio (with albumin measured in milligrams and creatinine measured in grams) of at least 200. Patients were randomly assigned to receive empagliflozin (10 mg once daily) or matching placebo. The primary outcome was a composite of progression of kidney disease (defined as end-stage kidney disease, a sustained decrease in eGFR to < 10 ml per minute per 1.73 m(2), a sustained decrease in eGFR of & GE;40% from baseline, or death from renal causes) or death from cardiovascular causes. Results A total of 6609 patients underwent randomization. During a median of 2.0 years of follow-up, progression of kidney disease or death from cardiovascular causes occurred in 432 of 3304 patients (13.1%) in the empagliflozin group and in 558 of 3305 patients (16.9%) in the placebo group (hazard ratio, 0.72; 95% confidence interval [CI], 0.64 to 0.82; P < 0.001). Results were consistent among patients with or without diabetes and across subgroups defined according to eGFR ranges. The rate of hospitalization from any cause was lower in the empagliflozin group than in the placebo group (hazard ratio, 0.86; 95% CI, 0.78 to 0.95; P=0.003), but there were no significant between-group differences with respect to the composite outcome of hospitalization for heart failure or death from cardiovascular causes (which occurred in 4.0% in the empagliflozin group and 4.6% in the placebo group) or death from any cause (in 4.5% and 5.1%, respectively). The rates of serious adverse events were similar in the two groups. Conclusions Among a wide range of patients with chronic kidney disease who were at risk for disease progression, empagliflozin therapy led to a lower risk of progression of kidney disease or death from cardiovascular causes than placebo