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Older adults are characterized by profound clinical heterogeneity. When designing and delivering interventions, there exist multiple approaches to account for heterogeneity. We present the results of a systematic review of data-driven, personalized interventions in older adults, which serves as a use case to distinguish the conceptual and methodologic differences between individualized intervention delivery and precision health-derived interventions. We define individualized interventions as those where all participants received the same parent intervention, modified on a case-by-case basis and using an evidence-based protocol, supplemented by clinical judgment as appropriate, while precision health-derived interventions are those that tailor care to individuals whereby the strategy for how to tailor care was determined through data-driven, precision health analytics. We discuss how their integration may offer new opportunities for analytics-based geriatric medicine that accommodates individual heterogeneity but allows for more flexible and resource-efficient population-level scaling.

It is unclear which energy expenditure prediction equation should guide weight loss interventions in older adults with obesity. We ascertained the validity of four equations commonly used in practice in a series of weight loss studies of adults aged ≥65 with a body mass index ≥30kg/m(2) using indirect calorimetry data. Diagnostic accuracy was defined as

BACKGROUND: Declining mortality rates and an aging population have contributed to increasing rates of multimorbidity (MM) in the United States. MM is strongly associated with a decline in physical function. Obesity is an important risk factor for the development of MM, and its prevalence continues to rise. Our study aimed to evaluate the associations between obesity, MM, and rates of functional limitations in older adults. METHODS: We analyzed body mass index (BMI) and self-reported comorbidity data from 7,261 individuals aged ≥ 60 years from the National Health and Nutrition Examination Surveys 2005–2014. Weight status was defined based on standard BMI categories. MM was defined as 2 or more comorbidities, while functional limitations were self-reported. Adjusted logistic regression quantified the association between standard BMI categories and MM. We also examined the difference in the prevalence of limitations between those with and without MM. RESULTS: The overall proportion of individuals with concomitant MM and obesity was 27.0%. Compared to a normal BMI, older adults with obesity had higher odds of MM (Prevalence odds ratio 1.79, 95% CI 1.49, 2.12). Overall, 67.5% of patients with MM also reported a functional limitation, with rates of functional limitation increasing with increasing BMI. When evaluating functional limitations in those with MM by BMI class, 90% of patients classified as severely obese (BMI ≥ 40 kg/m(2)) with MM also had a concomitant functional limitation. CONCLUSIONS: Compared to normal weight status, obesity is associated with an increased burden of MM and functional limitation among older adults. Our results underscore the importance of identifying and addressing obesity, MM, and functional limitation patterns and the need for evidence-based interventions that address all three conditions in this population.

Background There is a close relationship between weight status and cognitive impairment in older adults. This study examined the association between weight status and the trajectory of cognitive decline over time in a population-based cohort of older adults in China. Methods We used data from adults aged ≥55 years participating in the China health and nutrition survey (1997–2018). Underweight (body mass index [BMI] ≤ 18.5 kg/m2), normal weight (18.5–23 kg/m2), overweight (23–27.5 kg/m2), and obesity (BMI ≥ 27.5 kg/m2) were defined using the World Health Organization Asian cutpoints. Global cognition was estimated every 2–4 years through a face-to-face interview using a modified telephone interview for cognitive status (scores 0–27). The association between BMI and the rate of global cognitive decline, using a restricted cubic spline for age and age category, was examined with linear mixed-effects models accounting for correlation within communities and individuals. Results We included 5 992 adults (53% female participants, mean age of 62 at baseline). We found differences in the adjusted rate of global cognitive decline by weight status (p = .01 in the cubic spline model). Models were adjusted for sex, marital status, current employment status, income, region, urbanization, education status, birth cohort, leisure activity, smoking status, and self-reported diagnosis of hypertension, diabetes, or Myocardial Infarction (MI)/stroke. In addition, significant declines by age in global cognitive function were found for all weight status categories except individuals with obesity. Conclusions In a cohort of adults in China, cognitive decline trajectory differed by weight status. A slower rate of change was observed in participants classified as having obesity.

The European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) launched the Sarcopenic Obesity Global Leadership Initiative (SOGLI) to reach expert consensus on a definition and diagnostic criteria for Sarcopenic Obesity (SO). The present paper describes the proceeding of the Sarcopenic Obesity Global Leadership Initiative (SOGLI) meeting that was held on November 25th and 26th, 2022 in Rome, Italy. This consortium involved the participation of 50 researchers from different geographic regions and countries. The document outlines an agenda advocated by the SOGLI expert panel regarding the pathophysiology, screening, diagnosis, staging and treatment of SO that needs to be prioritized for future research in the field.

Summary Background & aims Weight loss in older adults enhances physical function, but may lead to sarcopenia and osteoporosis. Whey protein is a low cost rich source of essential amino acids, may improve physical function. We evaluated the feasibility and acceptability of consuming whey protein in the context of a weight-loss intervention in older adults with obesity. Methods A 12-week pilot feasibility, non-randomized weight loss study of 28 older adults was conducted, consisting of individualized, weekly dietitian visits with twice weekly physical therapist-led group strengthening classes. Half consumed whey protein, three times weekly, following exercise. Preliminary efficacy measures of body composition, sit-to-stand, 6-min walk and grip strength and subjective measures of self-reported health and function were also evaluated. Results Of the 37 enrolled, 28 completed the study (50 % in the protein group). Attendance rates for protein vs. non-protein groups were 89.9 ± 11.1 % vs. 95.6 ± 3.4 % (p = 0.08). Protein consumption was high in those attending classes (90.3 %) as was compliance at home (82.6 %). Whey was pleasant (67.3 ± 22.1, range 30–100, above average), had little aftertaste, and was neither salty or sticky. All were compliant (0.64 ± 0.84, range 0–5, low = higher compliance). Both groups lost significant weight (protein vs. no protein, −3.45 ± 2.86 vs. −5.79 ± 3.08, p = 0.47); Sit-to-stand, 6-min walk, and gait speed were no different, grip strength was improved in the protein compared to the non-protein group (−2.63 kg vs. 4.29 kg; p Conclusions Our results suggest that whey protein is a low-cost and readily available nutritional supplement that can be integrated into a weight loss intervention.

Medicare annual wellness visits (AWV) were initiated 10 years ago. Though AWVs emphasize on disease prevention and health promotion for older adults was a huge step forward, the current "one size fits all" approach does not adequately meet the wellness needs of a diverse population of older adults. Current AWVs do not sufficiently take into consideration the medical, psychological, functional, racial, cultural and socio-economic diversity of older adults. Updated AWVs should be tailored to meet the needs and priorities of older adults receiving them. Several geriatrics approaches to care, including geriatrics Glidepaths and the 4Ms of an Age-Friendly Health System, could help develop and guide a more patient-specific geriatrics focused approach to AWVs. Medicare's IPPE is an ideal time to advise new Medicare beneficiaries regarding what they should and should not do to maximize their ability to be healthy and functionally independent into their 80s, 90s, and 100s.

BACKGROUND/OBJECTIVES The number of older adults with complex health needs is growing, and this population experiences disproportionate morbidity and mortality. Interventions led by community health workers (CHWs) can improve clinical outcomes in the general adult population with multimorbidity, but few studies have investigated CHW-delivered interventions in older adults. DESIGN We systematically reviewed the impact of CHW interventions on health outcomes among older adults with complex health needs. We searched for English-language articles from database inception through April 2020 using seven databases. PROSPERO protocol registration CRD42019118761. SETTING Any U.S. or international setting, including clinical and community-based settings. PARTICIPANTS Adults aged 60 years or older with complex health needs, defined in this review as multimorbidity, frailty, disability, or high-utilization. INTERVENTIONS Interventions led by a CHW or similar role consistent with the American Public Health Association's definition of CHWs. MEASUREMENTS Pre-defined health outcomes (chronic disease measures, general health measures, treatment adherence, quality of life, or functional measures) as well as qualitative findings. RESULTS Of 5671 unique records, nine studies met eligibility criteria, including four randomized controlled trials, three quasi-experimental studies, and two qualitative studies. Target population and intervention characteristics were variable, and studies were generally of low-to-moderate methodological quality. Outcomes included mood, functional status and disability, social support, well-being and quality of life, medication knowledge, and certain health conditions (e.g., falls, cognition). Results were mixed with several studies demonstrating significant effects on mood and function, including one high-quality RCT, while others noted no significant intervention effects on outcomes. CONCLUSION CHW-led interventions may have benefit for older adults with complex health needs, but additional high-quality studies are needed to definitively determine the effectiveness of CHW interventions in this population. Integration of CHWs into geriatric clinical settings may be a strategy to deliver evidence-based interventions and improve clinical outcomes in complex older adults.

The population of older adults living with multiple chronic conditions (MCC) continues to grow. MCC is independently associated with functional limitation and obesity. The aim of our study was to evaluate the association between obesity and MCC, and secondarily, the combined presence of obesity and functional limitations with MCC.We analyzed cross-sectional survey data from the National Health and Aging Trends Survey (NHATS) 2011 baseline data, a nationally representative Medicare beneficiary cohort of adults in the United States. We evaluated the coexistent prevalence of obesity and MCC overall, and by standard body mass index (BMI) categories. We then evaluated the prevalence of functional limitations (mobility, self-care, and household activities) and Fried-defined frailty status in persons with a BMI ≥ 30 kg/mIn the 6,600 participants, the prevalence of concurrent obesity and MCC was 30.4%. Of those with obesity, the prevalence of MCC was 84.0%, and were more likely to have MCC (adjusted OR: 2.17, 95% CI 1.86, 2.54) compared to a normal BMI. Obesity and functional limitations or frailty were more likely have MCC than individuals with obesity alone.We found that individuals with obesity is strongly associated with MCC and that functional limitations and frailty status have a greater association with having MCC than individuals with obesity without MCC. Future longitudinal analyses are needed to ascertain this relationship.

Objective: To determine where the current literature stands in regard to diet/exercise interventions on cognition in overweight or obese individuals. Data Source: A rapid review was conducted of English-language studies published in Medline from January 1965 to January 2020. Study Inclusion and Exclusion Criteria: Included studies were intervention studies lasting ≥12 weeks, with participants aged ≥65 years, with a body mass index ≥25 kg/m2 Data Extraction: Data extracted included study population, duration, intervention design, outcomes, and results. Data Synthesis: Outcomes were qualitatively measured due to paucity of RTC. Results: 1845 citations were identified, 31 full-text articles were reviewed, and 5 studies were included. Studies had usual care control groups and combined exercise/diet intervention groups with 31-3,526 participants randomized to each arm. Mean age of participants was 69.2-83.4 years. Studies reporting on cognitive changes showed marginally significant positive changes in cognition, and those that reported BMI indicated potential improvements in cognition. Conclusions: The number of interventions assessing the combined effects of both diet and exercise is low. Future studies should evaluate the impact of combined effects to ascertain whether cognitive decline may be reversed in older adults with a BMI ≥25 kg/m2.

BACKGROUND AND AIMS: Obesity significantly impacts older adults. Intensive nutrition counseling can aid in weight reduction and improve diet quality, but data are sparse in this population. The objective of this intervention is to determine how intensive nutrition counseling affects diet quality and anthropometric measures during a multi-component weight loss intervention in rural older adults with obesity. METHODS: A series of 12-week, single-arm feasibility pilots were conducted in fall 2017 and winter/spring 2018 in a community aging center in rural Northern New England. Adults were eligible if ≥65 years old with a Body Mass Index (BMI) ≥30kg/m(2). Exclusion criteria included dementia/cognitive impairment, uncontrolled psychiatric illness, weight-loss surgery, weight loss >5% in previous 6-months, life-threatening illness, palliative/hospice services, current participation in another weight-loss study/program, obesogenic medications, or presence of major chronic conditions. Participants received once-weekly nutrition counseling by a registered dietitian nutritionist (RDN), and twice-weekly exercise sessions by a physical therapist (PT). Primary outcomes were diet quality changes measured by total Rapid Eating and Activity Assessment for Patients-Short Version (REAP-S) and Automated Self-Administered 24-hour dietary recall (ASA-24). Secondary outcome measures were changes in weight (kilograms) and waist circumference (centimeters). McNemar test was conducted for all paired categorical data while paired t-tests were conducted for all paired continuous data. All analyses were conducted in R; p-value

The growing population of older adults (age ≥65 years) is expected to lead to higher rates of cardiovascular disease. The expansion of digital health (encompassing telehealth, telemedicine, mobile health, and remote patient monitoring), Internet access, and cellular technologies provides an opportunity to enhance patient care and improve health outcomes—opportunities that are particularly relevant during the current coronavirus disease-2019 pandemic. Insufficient dexterity, visual impairment, and cognitive dysfunction, found commonly in older adults should be taken into consideration in the development and utilization of existing technologies. If not implemented strategically and appropriately, these can lead to inequities propagating digital divides among older adults, across disease severities and socioeconomic distributions. A systematic approach, therefore, is needed to study and implement digital health strategies in older adults. This review will focus on current knowledge of the benefits, barriers, and use of digital health in older adults for cardiovascular disease management.

The population worldwide is aging and prevalence of obesity in this population is increasing. The range of consequences that effect these at-risk patients include increased risk of falls, fractures, reduced quality of life, and cognitive decline. This article describes the epidemiology of obesity, risks and benefits of weight loss, and importance of treating obesity to help promote healthy aging. Health care professionals should encourage older adults with obesity to implement healthy lifestyle behaviors including exercise and diet routine. Treating obesity in older adults mitigates the significant public health crisis, and reduces health care utilization and risk of long-term adverse events.

Introduction Effective weight-management interventions require frequent interactions with specialised multidisciplinary teams of medical, nutritional and behavioural experts to enact behavioural change. However, barriers that exist in rural areas, such as transportation and a lack of specialised services, can prevent patients from receiving quality care. Methods We recruited patients from the Dartmouth-Hitchcock Weight & Wellness Center into a single-arm, non-randomised study of a remotely delivered 16-week evidence-based healthy lifestyle programme. Every 4 weeks, participants completed surveys that included their willingness to pay for services like those experienced in the intervention. A two-item Willingness-to-Pay survey was administered to participants asking about their willingness to trade their face-to-face visits for videoconference visits based on commute and copay. Results Overall, those with a travel duration of 31–45 min had a greater willingness to trade in-person visits for telehealth than any other group. Participants who had a travel duration less than 15 min, 16–30 min and 46–60 min experienced a positive trend in willingness to have telehealth visits until Week 8, where there was a general negative trend in willingness to trade in-person visits for virtual. Participants believed that telemedicine was useful and helpful. Conclusions In rural areas where patients travel 30–45 min a telemedicine-delivered, intensive weight-loss intervention may be a well-received and cost-effective way for both patients and the clinical care team to connect.

OBJECTIVE: This systematic review examined the impact of health homes on cardiometabolic risk in adults with serious mental illness. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures were used to conduct the systematic review. Databases were searched for peer-reviewed articles published between 1946 and August 2018 that compared health homes to a control condition (e.g., usual care, secondary data analyses using matched samples). Participants, interventions, comparisons, outcomes, and study design criteria were used to assess study eligibility. Studies were assessed for methodological quality using the Quality Assessment of Before and After Studies With No Control Group, and the Quality Assessment of Controlled Intervention Studies. RESULTS: Eighteen studies (i.e., eleven observational, four quasi-experimental, three randomized controlled trials) reported on 17 health homes. Most studies reported increases in receipt of screening for cardiometabolic risk factors and service use. There was a modest reduction in selected cardiometabolic risk factors among people with serious mental illness, but clinical outcomes varied widely among studies. DISCUSSION: Health homes are associated with increased rates of cardiometabolic screening and service use. However, improvement in cardiometabolic risk factors varied across the studies and the clinical significance of these reductions is not clear. Peer support and self-management training may represent strategies to improve cardiometabolic risk factors. CONCLUSIONS: Co-location of services may not be enough to significantly impact cardiometabolic risk factors. Health homes may have a greater impact on clinical outcomes if they include: standardized screening; peer support and self-management training; intervention components that target interdependent risk factors.

BACKGROUND: Aging alters biological processes resulting in body fat redistribution, loss of lean muscle mass, and reduced muscle strength, termed sarcopenia. Nutrition is an important modifiable risk factor in the development of sarcopenia. Food insecurity refers to limited or uncertain access to enough food for an active, healthy life, and is prevalent among older adults. The objective of this study was to examine the relationship between food insecurity and probable sarcopenia in older adults. METHODS: We examined 3,632 adults ≥60 years old from the 2011–2014 National Health and Nutrition Examination Surveys (NHANES). For our analysis food insecurity was identified using the Food Security Survey Module (FSSM). The primary outcome was based on the Sarcopenia Definitions and Outcomes consortium (SDOC) definition. Secondary outcomes were based on three other different grip strength cut-offs as there is debate within the field as to the optimal definition of sarcopenia. Consistent with the revised European consensus on the definition and diagnosis of Sarcopenia (EWGSOP2) recommendations, we used the term probable sarcopenia throughout this text as definitions were based on muscle strength alone and did not include an evaluation of muscle quality. Sensitivity analyses were performed using the standard four category definition of food security. We used logistic regression to examine the association between food insecurity and sarcopenia. RESULTS: Using the Sarcopenia Definitions and Outcomes Consortium definition, 24.7% were classified as having probable sarcopenia (low grip strength); 5.5% had food insecurity and food insecurity was associated with probable sarcopenia (OR 1.51, 95%CI 1.03–2.22). Using three other definitions of probable sarcopenia, food insecurity was significantly associated with probable sarcopenia using the Foundation for the National Institute of Health definition using grip strength alone (OR 1.71, 95%CI 1.08–2.71), but food insecurity was not associated with food insecurity using definitions related to grip strength/BMI (OR 1.16, 95%CI 0.76–1.78) or grip strength/weight (OR 1.14, 95%CI 0.85–1.54). CONCLUSIONS: In this nationally representative cohort study, individuals classified as having food insecurity were more likely to have probable sarcopenia (low grip strength) compared to those with full food security. Future studies should examine whether food insecurity interventions may reduce probable sarcopenia and associated adverse outcomes.

Sarcopenia, defined as the loss of muscle mass, strength, and function with aging, is a geriatric syndrome with important implications for patients and healthcare systems. Sarcopenia increases the risk of clinical decompensation when faced with physiological stressors and increases vulnerability, termed frailty. Sarcopenia develops due to inflammatory, hormonal, and myocellular changes in response to physiological and pathological aging, which promote progressive gains in fat mass and loss of lean mass and muscle strength. Progression of these pathophysiological changes can lead to sarcopenic obesity and physical frailty. These syndromes independently increase the risk of adverse patient outcomes including hospitalizations, long-term care placement, mortality, and decreased quality of life. This risk increases substantially when these syndromes co-exist. While there is evidence suggesting that the progression of sarcopenia, sarcopenic obesity, and frailty can be slowed or reversed, the adoption of broad-based screening or interventions has been slow to implement. Factors contributing to slow implementation include the lack of cost-effective, timely bedside diagnostics and interventions that target fundamental biological processes. This paper describes how clinical, radiographic, and biological data can be used to evaluate older adults with sarcopenia and sarcopenic obesity and to further the understanding of the mechanisms leading to declines in physical function and frailty.

Continuous glucose monitoring (CGM) can reduce hypoglycemia in older adults with type 1 diabetes (T1D). We aimed to characterize factors that influence effective use in this age group.Older adults with type T1D (age ≥65) and their caregivers participated in one of a series of parallel group model building workshops, a participatory approach to system dynamics involving drawing and scripted group activities. Data were synthesized in a qualitative model of the hypothesized system of factors producing distinct patterns of CGM use in older adults. The model was validated through virtual follow-up interviews.Data were collected from 33 participants (four patient-caregiver dyads, mean age 73.8±4.4 years [range 66-85 years]; 16% non-CGM users, 79% pump users). The system model delineates drivers of CGM uptake, drivers of ongoing CGM use, and feedback loops that either reinforce or counteract future CGM use. Participants emphasized the importance of different sets of feedback loops at different points in the duration of CGM use.The holistic system model underscores that factors and feedback loops driving effective CGM use in older adults are both individualized and dynamic (e.g., changing over time), suggesting opportunities for staged and tailored age-specific education and support. Short title: CGM Use in Older Adults with Type 1 Diabetes.

Eliciting a weight history can provide clinically important information to aid in treatment decision-making. This view is consistent with the life course perspective of obesity and the aim of patient-centered care, one of six domains of health care quality. However, thus far, the value and practicality of including a weight history in the clinical assessment and treatment of patients with obesity have not been systematically explored. For these reasons, the Clinical Committee of The Obesity Society established a task force to review and assess the available evidence to address five key questions. It is concluded that weight history is an essential component of the medical history for patients presenting with overweight or obesity, and there are strong and emerging data that demonstrate the importance of life stage, duration of exposure to obesity, maximum BMI, and group-based trajectory modeling in predicting risk for increased morbidity and mortality. Consideration of these and other patient-specific factors may improve risk stratification and clinical decision-making for screening, counseling, and management. Recommendations are provided for the key elements that should be included in a weight history, and several needs for future clinical research are outlined.

BACKGROUND Older adults who engage in physical activity can reduce their risk of mobility and disability. Short amounts of walking can improve their 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 (e.g., Fitbit) are proprietary, often are not tailored to the movements of older adults and have been shown to be inaccurate in clinical settings. Few studies have developed step-counting algorithms for smartwatches – but only using data from younger adults and often validating them only in controlled laboratory settings. OBJECTIVE In this work, we sought to develop and validate a smartwatch step-counting app targeting older adults that has been evaluated in free-living settings over a long period of time (24 weeks) with a large sample (N=42). METHODS We developed and evaluated a step-counting app on the Amulet, an open-source wrist-worn device, to track the steps of older adults. The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm with a total of 42 older adults in the lab (counting 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 system development, we evaluated four kinds of walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field study, we evaluated various values for algorithm parameters, and subsequently evaluated the method’s performance using correlations and error rates. RESULTS The results from the usability evaluation showed that our step-counting algorithm performs well, highly correlated with the ground truth and with low error rate. For the lab study, there was stronger correlation for normal walking R2=0.5; across all activities, the Amulet was on average 3.2 (2.1%) steps lower (SD = 25.9) than video-validated steps. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2 of 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 of 0.669. CONCLUSIONS Our findings demonstrate the importance of an iterative process in algorithm development in advance of 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 improve the physical activity among older adults through accurate tracking of their step counts and in-app daily step-count goals. CLINICALTRIAL

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.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.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.The step-counting algorithm performed well. In the lab study, for normal walking (ROur 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.