Establishing cut-points for physical activity classification using triaxial accelerometer in middle-aged recreational marathoners

Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review

BACKGROUND

Monitoring free-living physical activity (PA) through wearable devices enables the real-time assessment of activity features associated with health outcomes and provision of treatment recommendations and adjustments. The conclusions of studies on PA and health depend crucially on reliable statistical analyses of digital data. Data analytics, however, are challenging due to the various metrics adopted for measuring PA, different aims of studies, and complex temporal variations within variables. The application, interpretation, and appropriateness of these analytical tools have yet to be summarized.

OBJECTIVE

This research aimed to review studies that used analytical methods for analyzing PA monitored by accelerometers. Specifically, this review addressed three questions: (1) What metrics are used to describe an individual's free-living daily PA? (2) What are the current analytical tools for analyzing PA data, particularly under the aims of classification, association with health outcomes, and prediction of health events? and (3) What challenges exist in the analyses, and what recommendations for future research are suggested regarding the use of statistical methods in various research tasks?

METHODS

This scoping review was conducted following an existing framework to map research studies by exploring the information about PA. Three databases, PubMed, IEEE Xplore, and the ACM Digital Library, were searched in February 2024 to identify related publications. Eligible articles were classification, association, or prediction studies involving human PA monitored through wearable accelerometers.

RESULTS

After screening 1312 articles, 428 (32.62%) eligible studies were identified and categorized into at least 1 of the following 3 thematic categories: classification (75/428, 17.5%), association (342/428, 79.9%), and prediction (32/428, 7.5%). Most articles (414/428, 96.7%) derived PA variables from 3D acceleration, rather than 1D acceleration. All eligible articles (428/428, 100%) considered PA metrics represented in the time domain, while a small fraction (16/428, 3.7%) also considered PA metrics in the frequency domain. The number of studies evaluating the influence of PA on health conditions has increased greatly. Among the studies in our review, regression-type models were the most prevalent (373/428, 87.1%). The machine learning approach for classification research is also gaining popularity (32/75, 43%). In addition to summary statistics of PA, several recent studies used tools to incorporate PA trajectories and account for temporal patterns, including longitudinal data analysis with repeated PA measurements and functional data analysis with PA as a continuum for time-varying association (68/428, 15.9%).

CONCLUSIONS

Summary metrics can quickly provide descriptions of the strength, frequency, and duration of individuals' overall PA. When the distribution and profile of PA need to be evaluated or detected, considering PA metrics as longitudinal or functional data can provide detailed information and improve the understanding of the role PA plays in health. Depending on the research goal, appropriate analytical tools can ensure the reliability of the scientific findings.

Prehabilitation Consultation on Self-Care and Physical Exercise in Patients Diagnosed with Abdominopelvic Cancer: Protocol of the Study

BACKGROUND

Introduction: Prehabilitation in the field of oncology has been defined as "the process in the continuum of care that occurs between diagnosis and the start of treatment involving physical and psychological measures that determine the patient's baseline functional status."

AIM

To determine the effectiveness of a Prehabilitation consultation on self-care and targeted physical exercise for patients diagnosed with abdominopelvic cancer.

DESIGN

An observational study that will evaluate the pre-post efficacy of an ad-hoc designed Prehabilitation intervention. The study population consists of patients diagnosed with colon or gynecological cancer with a surgical indication as part of their therapeutic plan from the General Surgery Services. It is configured around four key interventions: (a) health education and self-care, (b) specific nutritional counseling, (c) initial psychological assessment, and (d) directed physical exercise intervention. Health education, self-care interventions, and physical exercise will be carried out weekly from diagnosis to the scheduled surgery day.

RESULTS

Aspects such as self-care capacity or agency, perioperative anxiety, aerobic capacity, strength and flexibility, postoperative complications, and recovery time to adjuvant treatment will be measured using tools such as Appraisal of self-care agency scale (ASA), State Trait Anxiety Inventory (STAI), walking test, sit and Reach, Hand Grip or Squad Jump.

CONCLUSION

Utilizing validated tools for analyzing selected variables will contribute to refining and expanding care guidelines, ultimately enhancing support for both patients and their caregivers.

Physical Activity Intensity Measurement and Health: State of the Art and Future Directions for Scientific Research

Physical activity guidelines for health recommend any type of unstructured physical activity for health promotion. Adults should perform at least 150-300 min per week of moderate intensity or 75-150 min per week of vigorous intensity activities, or an equivalent combination of the two intensities. However, the relationship between physical activity intensity and longevity remains a debated topic, with conflicting perspectives offered by epidemiologists, clinical exercise physiologists or anthropologists. This paper addresses the current known role of physical activity intensity (in particular vigorous versus moderate intensity) on mortality and the existing problems of measurement. Given the diversity of existing proposals to categorize physical activity intensity, we call for a common methodology. Device-based physical activity measurements (e.g., wrist accelerometers) have been proposed as a valid method to measure physical activity intensity. An appraisal of the results reported in the literature, however, highlights that wrist accelerometers have not yet demonstrated sufficient criterion validity when they are compared to indirect calorimetry. Novel biosensors and wrist accelerometers will help us understand how different metrics of physical activity relates to human health, however, all these technologies are not enough mature to provide personalized applications for healthcare or sports performance.

Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range

Accelerometer data can be used to estimate incident oxygen consumption (VO2) during physical activity. Relationships between the accelerometer metrics and VO2 are typically determined using specific walking or running protocols on a track or treadmill. In this study, we compared the predictive performance of three different metrics based on the mean amplitude deviation (MAD) of the raw three-dimensional acceleration signal during maximal tests performed on a track or treadmill. A total of 53 healthy adult volunteers participated in the study, 29 performed the track test and 24 the treadmill test. During the tests, the data were collected using hip-worn triaxial accelerometers and metabolic gas analyzers. Data from both tests were pooled for primary statistical analysis. For typical walking speeds at VO2 less than 25 mL/kg/min, accelerometer metrics accounted for 71-86% of the variation in VO2. For typical running speeds starting from VO2 of 25 mL/kg/min up to over 60 mL/kg/min, 32-69% of the variation in VO2 could be explained, while the test type had an independent effect on the results, except for the conventional MAD metrics. The MAD metric is the best predictor of VO2 during walking, but the poorest during running. Depending on the intensity of locomotion, the choice of proper accelerometer metrics and test type may affect the validity of the prediction of incident VO2.

Physical Activity Monitoring and Classification Using Machine Learning Techniques

Physical activity plays an important role in controlling obesity and maintaining healthy living. It becomes increasingly important during a pandemic due to restrictions on outdoor activities. Tracking physical activities using miniature wearable sensors and state-of-the-art machine learning techniques can encourage healthy living and control obesity. This work focuses on introducing novel techniques to identify and log physical activities using machine learning techniques and wearable sensors. Physical activities performed in daily life are often unstructured and unplanned, and one activity or set of activities (sitting, standing) might be more frequent than others (walking, stairs up, stairs down). None of the existing activities classification systems have explored the impact of such class imbalance on the performance of machine learning classifiers. Therefore, the main aim of the study is to investigate the impact of class imbalance on the performance of machine learning classifiers and also to observe which classifier or set of classifiers is more sensitive to class imbalance than others. The study utilizes motion sensors' data of 30 participants, recorded while performing a variety of daily life activities. Different training splits are used to introduce class imbalance which reveals the performance of the selected state-of-the-art algorithms with various degrees of imbalance. The findings suggest that the class imbalance plays a significant role in the performance of the system, and the underrepresentation of physical activity during the training stage significantly impacts the performance of machine learning classifiers.

Renal Function Recovery Strategies Following Marathon in Amateur Runners

Long distance races have a physiological impact on runners. Up to now, studies analyzing these physiological repercussions have been mainly focused on muscle and cardiac damage, as well as on its recovery. Therefore, a limited number of studies have been done to explore acute kidney failure and recovery after performing extreme exercises. Here, we monitored renal function in 76 marathon finishers (14 females) from the day before participating in a marathon until 192 h after crossing the finish line (FL). Renal function was evaluated by measuring serum creatinine (sCr) and the glomerular filtration rate (GFR). We randomly grouped our cohort into three intervention groups to compare three different strategies for marathon recovery: total rest (REST), continuous running at their ventilatory threshold 1 (VT1) intensity (RUN), and elliptical workout at their VT1 intensity (ELLIPTICAL). Interventions in the RUN and ELLIPTICAL groups were performed at 48, 96, and 144 h after marathon running. Seven blood samples (at the day before the marathon, at the FL, and at 24, 48, 96, 144, and 192 h post-marathon) and three urine samples (at the day before the marathon, at the finish line, and at 48 h post-marathon) were collected per participant. Both heart rate monitors and triaxial accelerometers were used to control the intensity effort during both the marathon race and the recovery period. Contrary to our expectations, the use of elliptical machines for marathon recovery delays renal function recovery. Specifically, the ELLIPTICAL group showed a significantly lower ∆GFR compared to both the RUN group (p = 4.5 × 10−4) and the REST group (p = 0.003). Hence, we encourage runners to carry out an active recovery based on light-intensity continuous running from 48 h after finishing the marathon. In addition, full resting seems to be a better strategy than performing elliptical workouts.

Pacing and Body Weight Changes During a Mountain Ultramarathon: Sex Differences and Performance

The study was aimed at comparing pacing adopted by males and females in a 107-km mountain ultramarathon and assessing whether pacing-related variables were associated with intracompetition body weight changes and performance. Forty-seven athletes (29 males; 18 females) were submitted to a cardiopulmonary exercise test before the race. Athletes were also weighted before the start of the race, at three midpoints (33 km, 66 km and 84 km) and after the race. Pacing was analyzed using absolute and relative speeds and accelerometry-derived sedentary time spent during the race. Results showed that females spent less sedentary time (4.72 ± 2.91 vs. 2.62 ± 2.14%; p = 0.035; d = 0.83) and displayed a smaller body weight loss (3.01 ± 1.96 vs. 4.37 ± 1.77%; p = 0.048; d = 0.77) than males. No significant sex differences were revealed for speed variability, absolute and relative speed. In addition, finishing time was correlated with: speed variability (r = 0.45; p = 0.010), index of pacing (r = -0.63; p < 0.001) and sedentary time (r = 0.64; p < 0.001). Meanwhile, intracompetition body weight changes were related with both the absolute and relative speed in the first and the last race section. These results suggest that females, as compared with males, take advantage of shorter time breaks at aid stations. Moreover, performing a more even pacing pattern may be positively associated with performance in mountain ultramarathons. Finally, intracompetition body weight changes in those races should be considered in conjunction with running speed fluctuations.

Triaxial Accelerometry-Based Moderate to Vigorous Physical Activity in Older Adults Using Four Different Methods

The amount of physical activity reported using accelerometry can vary depending on the method used. This study examined variability in four different methods of calculating moderate to vigorous physical activity (MVPA) among older adults, as well as lifestyle correlates of physical activity. The MVPA data were captured (n = 111; Mage = 70.3 years, SDage = 6.3) using waist-worn ActiGraph wGT3X-BT monitors and examined using 10-min bouted versus sporadic methods, and with cut points calibrated to older and younger adults. The sample, on average, did not meet national guidelines of 150 min/week of MVPA when using bouted methods, irrespective of cut point used. This was not the case for sporadic MVPA. More physical activity was reported for participants with two or more physical hobbies, but no association with social behavior was found. These results demonstrate the wide variability possible in reporting methods for accelerometry data and their relation to adherence rates for national health recommendations.

Assessment of Physical Activity in Adults using Wrist Accelerometers

The health benefits of physical activity have been widely recognized, yet traditional measures of physical activity including questionnaires and category-based assessments of volume and intensity provide only broad estimates of daily activities. Accelerometers have advanced epidemiologic research on physical activity by providing objective and continuous measurement of physical activity in free-living conditions. Wrist-worn accelerometers have become especially popular due to low participant burden. However, the validity and reliability of wrist-worn devices for adults have yet to be summarized. Moreover, accelerometer data provide rich information on how physical activity is accumulated throughout the day, but only a small portion of these rich data have been utilized by researchers. Lastly, new methodological developments that aim to overcome some of the limitations of accelerometers are emerging. The purpose of this review is to provide an overview of accelerometry research, with a special focus on wrist-worn accelerometers. We describe briefly how accelerometers work, summarize the validity and reliability of wrist-worn accelerometers, discuss the benefits of accelerometers including measuring light-intensity physical activity, and discuss pattern metrics of daily physical activity recently introduced in the literature. A summary of large-scale cohort studies and randomized trials that implemented wrist-worn accelerometry is provided. We conclude the review by discussing new developments and future directions of research using accelerometers, with a focus on wrist-worn accelerometers.

Recovery of Inflammation, Cardiac, and Muscle Damage Biomarkers After Running a Marathon

ABSTRACT

Bernat-Adell, MD, Collado-Boira, EJ, Moles-Julio, P, Panizo-González, N, Martínez-Navarro, I, Hernando-Fuster, B, and Hernando-Domingo, C. Recovery of inflammation, cardiac and muscle damage biomarkers after running a marathon. J Strength Cond Res 35(3): 626-632, 2021-Physical endurance sports conditions the increase of blood biomarkers responsible for the acute inflammatory response. The purpose of this study was to observe the impact of intense physical exercise on these biomarkers and detect their recovery pattern. This is an experimental study of repeated measures (pre-post marathon). The biomarkers lactate dehydrogenase (LDH), creatine kinase (CK), high-sensitivity troponin T (hs-TNT), and C-reactive protein (CRP) were analyzed in a total of 86 runners, 24 hours before the marathon, immediately after finishing the race and at 24, 48, 96, and 144 postrace hours. The comparative analyses were performed using the Friedman and Wilcoxon tests. The correlations between dependent and independent variables were analyzed using Spearman correlations. The data were processed through the IBM SPSS package, version 23. Significant value was p ≤ 0.05. The LDH increased and showed significant differences (p ≤ 0.001) for all times, compared with the initial LDH value, normalizing after 192 hours (p = 0.667) (effect size [ES], r = 0.807). The CK increased and showed significant differences (p ≤ 0.001) (ES, r = 0.975) up to 96 hours afterward, normalizing after 144 hours. The hs-TNT presented an increase and showed significant differences (p ≤ 0.001) between the pre-post race times, 24 and 48 hours, normalizing after 96 hours, although it showed a new significant value at 192 hours (p ≤ 0.001) (ES, r = 0.519). The CRP increased and showed significant differences (p ≤ 0.001) between the pre-post race times, at 24, 48, 96, 144, and 192 hours after race. The recovery after alterations produced by the marathon varies according to the biomarker. Blood levels of biomarkers decrease with longer race times. Greater energy expenditure increases the blood levels of LDH, CK, and hs-TNT.

Inflammation, muscle damage and postrace physical activity following a mountain ultramarathon

BACKGROUND

The study aimed at exploring whether muscle membrane disruption, as a surrogate for muscle damage, and inflammation recovery following a mountain ultramarathon (MUM) was related with race performance and postrace physical activity.

METHODS

Blood samples were obtained from thirty-four athletes (29 men and 5 women) before a 118-km MUM, immediately after and three- and seven-days postrace. Creatine kinase (CK), lactate dehydrogenase (LDH) and C-reactive protein (CRP) were compared between faster (FR) and slower (SR) runners. Physical activity performed during the week following the MUM was objectively analyzed using accelerometers and compared between FR and SR.

RESULTS

CK was significantly higher in FR at 3 days postrace (P<0.012, d=1.17) and LDH was significantly higher in FR at 3- and 7-days postrace (P=0.005, d=1.01; P<0.015, d=1.05 respectively), as compared to SR. No significant differences were identified in postrace physical activity levels between FR and SR. Significant relationships were found between race time and CK and LDH concentrations at 3 days postrace (r<inf>s</inf>=-0.41, P=0.017; r<inf>s</inf>=-0.52, P=0.002 respectively) and 7 days postrace (r<inf>s</inf>=-0.36, P=0.039; r<inf>s</inf>=-0.46. P=0.007 respectively). However, postrace physical activity was not associated with muscle damage and inflammation recovery, except for light intensity and CRP at 3 days postrace (r<inf>s</inf>=-0.40, P=0.025).

CONCLUSIONS

Race time appeared to have a higher influence on muscle damage recovery than the intensity of physical activities performed in the week after running a MUM. Inflammatory activity takes longer to normalize than muscle damage following a MUM, it is not related with race time and lightly related with postrace physical activity.

Using Accelerometry for Evaluating Energy Consumption and Running Intensity Distribution Throughout a Marathon According to Sex

The proportion of females participating in long-distance races has been increasing in the last years. Although it is well-known that there are differences in how females and males face a marathon, higher research may be done to fully understand the intrinsic and extrinsic factors affecting sex differences in endurance performance. In this work, we used triaxial accelerometer devices to monitor 74 males and 14 females, aged 30 to 45 years, who finished the Valencia Marathon in 2016. Moreover, marathon split times were provided by organizers. Several physiological traits and training habits were collected from each participant. Then, we evaluated several accelerometry- and pace-estimated parameters (pacing, average change of speed, energy consumption, oxygen uptake, running intensity distribution and running economy) in female and male amateur runners. In general, our results showed that females maintained a more stable pacing and ran at less demanding intensity throughout the marathon, limiting the decay of running pace in the last part of the race. In fact, females ran at 4.5% faster pace than males in the last kilometers. Besides, their running economy was higher than males (consumed nearly 19% less relative energy per distance) in the last section of the marathon. Our results may reflect well-known sex differences in physiology (i.e., muscle strength, fat metabolism, VO_2max), and in running strategy approach (i.e., females run at a more conservative intensity level in the first part of the marathon compared to males). The use of accelerometer devices allows coaches and scientific community to constantly monitor a runner throughout the marathon, as well as during training sessions.

Physiological and Race Pace Characteristics of Medium and Low-Level Athens Marathon Runners

This study examined physiological and race pace characteristics of medium- (finish time < 240 min) and low-level (finish time > 240 min) recreational runners who participated in a challenging marathon route with rolling hills, the Athens Authentic Marathon. Fifteen athletes (age: 42 ± 7 years) performed an incremental test, three to nine days before the 2018 Athens Marathon, to determine maximal oxygen uptake (VO2 max), maximal aerobic velocity (MAV), energy cost of running (ECr) and lactate threshold velocity (vLTh), and were analyzed for their pacing during the race. Moderate- (n = 8) compared with low-level (n = 7) runners had higher (p < 0.05) VO2 max (55.6 ± 3.6 vs. 48.9 ± 4.8 mL·kg-1·min-1), MAV (16.5 ± 0.7 vs. 14.4 ± 1.2 km·h-1) and vLTh (11.6 ± 0.8 vs. 9.2 ± 0.7 km·h-1) and lower ECr at 10 km/h (1.137 ± 0.096 vs. 1.232 ± 0.068 kcal·kg-1·km-1). Medium-level runners ran the marathon at a higher percentage of vLTh (105.1 ± 4.7 vs. 93.8 ± 6.2%) and VO2 max (79.7 ± 7.7 vs. 68.8 ± 5.7%). Low-level runners ran at a lower percentage (p < 0.05) of their vLTh in the 21.1-30 km (total ascent/decent: 122 m/5 m) and the 30-42.195 km (total ascent/decent: 32 m/155 m) splits. Moderate-level runners are less affected in their pacing than low-level runners during a marathon route with rolling hills. This could be due to superior physiological characteristics such as VO2 max, ECr, vLTh and fractional utilization of VO2 max. A marathon race pace strategy should be selected individually according to each athlete's level.

Muscle Cramping in the Marathon: Dehydration and Electrolyte Depletion vs. Muscle Damage

Martínez-Navarro, I, Montoya-Vieco, A, Collado, E, Hernando, B, Panizo, N, and Hernando, C. Muscle Cramping in the marathon: Dehydration and electrolyte depletion vs. muscle damage. J Strength Cond Res XX(X): 000-000, 2020-Our aim was to compare dehydration variables, serum electrolytes, and muscle damage serum markers between runners who suffered exercise-associated muscle cramps (EAMC) and runners who did not suffer EAMC in a road marathon. We were also interested in analyzing race pacing and training background. Nighty-eight marathoners took part in the study. Subjects were subjected to a cardiopulmonary exercise test. Before and after the race, blood and urine samples were collected and body mass (BM) was measured. Immediately after the race EAMC were diagnosed. Eighty-eight runners finished the marathon, and 20 of them developed EAMC (24%) during or immediately after the race. Body mass change, post-race urine specific gravity, and serum sodium and potassium concentrations were not different between crampers and noncrampers. Conversely, runners who suffered EAMC exhibited significantly greater post-race creatine kinase (464.17 ± 220.47 vs. 383.04 ± 253.41 UI/L, p = 0.034) and lactate dehydrogenase (LDH) (362.27 ± 72.10 vs. 307.87 ± 52.42 UI/L, p = 0.002). Twenty-four hours post-race also values of both biomarkers were higher among crampers (CK: 2,438.59 ± 2,625.24 vs. 1,166.66 ± 910.71 UI/L, p = 0.014; LDH: 277.05 ± 89.74 vs. 227.07 ± 37.15 UI/L, p = 0.021). The difference in the percentage of runners who included strength conditioning in their race training approached statistical significance (EAMC: 25%, non-EAMC: 47.6%; p = 0.074). Eventually, relative speed between crampers and noncrampers only differed from the 25th km onward (p < 0.05). Therefore, runners who suffered EAMC did not exhibit a greater degree of dehydration and electrolyte depletion after the marathon but displayed significantly higher concentrations of muscle damage biomarkers.

Immediate and 24-h post-marathon cardiac troponin T is associated with relative exercise intensity

PURPOSE

We aimed at exploring whether cardiopulmonary fitness, echocardiographic measures and relative exercise intensity were associated with high-sensitivity cardiac troponin T (hs-TNT) rise and normalization following a marathon.

METHODS

Nighty-eight participants (83 men, 15 women; 38.72 ± 3.63 years) were subjected to echocardiographic assessment and a cardiopulmonary exercise test (CPET) before the race. hs-TNT was measured before, immediately after and at 24, 48, 96, 144 and 192 h post-race. Speed and mean heart rate (HR) during the race were relativized to CPET values: peak speed (%V_VMAX), peak HR (HR_%MAX), speed and HR at the second ventilatory threshold (HR_%VT2 and %V_VT2).

RESULTS

Hs-TNT increased from pre- to post-race (5.74 ± 5.29 vs. 50.4 ± 57.04 ng/L; p < 0.001), seeing values above the Upper Reference Limit (URL) in 95% of the participants. At 24 h post-race, 39% of the runners still exceeded the URL (High hs-TNT group). hs-TNT rise was correlated with marathon speed %V_VT2 (r = 0.22; p = 0.042), mean HR_%VT2 (r = 0.30; p = 0.007), and mean HR_%MAX (r = 0.32; p = 0.004). Moreover, the High hs-TNT group performed the marathon at a higher Speed %V_VT2 (88.21 ± 6.53 vs. 83.49 ± 6.54%; p = 0.002) and Speed %V_VMAX (72 ± 4.25 vs. 69.40 ± 5.53%; p = 0.009). hs-TNT showed no significant associations with cardiopulmonary fitness and echocardiographic measures, except for a slight correlation with left ventricular end systolic diameter (r = 0.26; p = 0.018).

CONCLUSION

Post-race hs-TNT was above the URL in barely all runners. Magnitude of hs-TNT rise was correlated with exercise mean HR; whereas, its normalization kept relationship with marathon relative speed.

Estimation of energy consumed by middle-aged recreational marathoners during a marathon using accelerometry-based devices

As long-distance races have substantially increased in popularity over the last few years, the improvement of training programs has become a matter of concern to runners, coaches and health professionals. Triaxial accelerometers have been proposed as a one of the most accurate tools to evaluate physical activity during free-living conditions. In this study, eighty-eight recreational marathon runners, aged 30–45 years, completed a marathon wearing a GENEActiv accelerometer on their non-dominant wrist. Energy consumed by each runner during the marathon was estimated based on both running speed and accelerometer output data, by applying the previously established GENEActiv cut-points for discriminating the six relative-intensity activity levels. Since accelerometry allowed to perform an individualized estimation of energy consumption, higher interpersonal differences in the number of calories consumed by a runner were observed after applying the accelerometry-based approach as compared to the speed-based method. Therefore, pacing analyses should include information of effort intensity distribution in order to adjust race pacing appropriately to achieve the marathon goal time. Several biomechanical and physiological parameters (maximum oxygen uptake, energy cost of running and running economy) were also inferred from accelerometer output data, which is of great value for coaches and doctors.

Optimization and Validation of an Adjustable Activity Classification Algorithm for Assessment of Physical Behavior in Elderly

Due to a lack of transparency in both algorithm and validation methodology, it is difficult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (±10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications.