Step Counter Use and Sedentary Time in Adults: A Meta-Analysis

Wearable Devices to Improve Physical Activity and Reduce Sedentary Behaviour: An Umbrella Review

BACKGROUND

Several systematic reviews (SRs), with and without meta-analyses, have investigated the use of wearable devices to improve physical activity, and there is a need for frequent and updated syntheses on the topic.

OBJECTIVE

We aimed to evaluate whether using wearable devices increased physical activity and reduced sedentary behaviour in adults.

METHODS

We conducted an umbrella review searching PubMed, Cumulative Index to Nursing and Allied Health Literature, the Cochrane Library, MedRxiv, Rxiv and bioRxiv databases up to February 5th, 2023. We included all SRs that evaluated the efficacy of interventions when wearable devices were used to measure physical activity in adults aged over 18 years. The primary outcomes were physical activity and sedentary behaviour measured as the number of steps per day, minutes of moderate to vigorous physical activity (MVPA) per week, and minutes of sedentary behaviour (SB) per day. We assessed the methodological quality of each SR using the Assessment of Multiple Systematic Reviews, version 2 (AMSTAR 2) and the certainty of evidence of each outcome measure using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations). We interpreted the results using a decision-making framework examining the clinical relevance and the concordances or discordances of the SR effect size.

RESULTS

Fifty-one SRs were included, of which 38 included meta-analyses (302 unique primary studies). Of the included SRs, 72.5% were rated as 'critically low methodological quality'. Overall, with a slight overlap of primary studies (corrected cover area: 3.87% for steps per day, 3.12% for MVPA, 4.06% for SB) and low-to-moderate certainty of the evidence, the use of WDs may increase PA by a median of 1,312.23 (IQR 627-1854) steps per day and 57.8 (IQR 37.7 to 107.3) minutes per week of MVPA. Uncertainty is present for PA in pathologies and older adults subgroups and for SB in mixed and older adults subgroups (large confidence intervals).

CONCLUSIONS

Our findings suggest that the use of WDs may increase physical activity in middle-aged adults. Further studies are needed to investigate the effects of using WDs on specific subgroups (such as pathologies and older adults) in different follow-up lengths, and the role of other intervention components.

AI and semantic ontology for personalized activity eCoaching in healthy lifestyle recommendations: a meta-heuristic approach

BACKGROUND

Automated coaches (eCoach) can help people lead a healthy lifestyle (e.g., reduction of sedentary bouts) with continuous health status monitoring and personalized recommendation generation with artificial intelligence (AI). Semantic ontology can play a crucial role in knowledge representation, data integration, and information retrieval.

METHODS

This study proposes a semantic ontology model to annotate the AI predictions, forecasting outcomes, and personal preferences to conceptualize a personalized recommendation generation model with a hybrid approach. This study considers a mixed activity projection method that takes individual activity insights from the univariate time-series prediction and ensemble multi-class classification approaches. We have introduced a way to improve the prediction result with a residual error minimization (REM) technique and make it meaningful in recommendation presentation with a Naïve-based interval prediction approach. We have integrated the activity prediction results in an ontology for semantic interpretation. A SPARQL query protocol and RDF Query Language (SPARQL) have generated personalized recommendations in an understandable format. Moreover, we have evaluated the performance of the time-series prediction and classification models against standard metrics on both imbalanced and balanced public PMData and private MOX2-5 activity datasets. We have used Adaptive Synthetic (ADASYN) to generate synthetic data from the minority classes to avoid bias. The activity datasets were collected from healthy adults (n = 16 for public datasets; n = 15 for private datasets). The standard ensemble algorithms have been used to investigate the possibility of classifying daily physical activity levels into the following activity classes: sedentary (0), low active (1), active (2), highly active (3), and rigorous active (4). The daily step count, low physical activity (LPA), medium physical activity (MPA), and vigorous physical activity (VPA) serve as input for the classification models. Subsequently, we re-verify the classifiers on the private MOX2-5 dataset. The performance of the ontology has been assessed with reasoning and SPARQL query execution time. Additionally, we have verified our ontology for effective recommendation generation.

RESULTS

We have tested several standard AI algorithms and selected the best-performing model with optimized configuration for our use case by empirical testing. We have found that the autoregression model with the REM method outperforms the autoregression model without the REM method for both datasets. Gradient Boost (GB) classifier outperforms other classifiers with a mean accuracy score of 98.00%, and 99.00% for imbalanced PMData and MOX2-5 datasets, respectively, and 98.30%, and 99.80% for balanced PMData and MOX2-5 datasets, respectively. Hermit reasoner performs better than other ontology reasoners under defined settings. Our proposed algorithm shows a direction to combine the AI prediction forecasting results in an ontology to generate personalized activity recommendations in eCoaching.

CONCLUSION

The proposed method combining step-prediction, activity-level classification techniques, and personal preference information with semantic rules is an asset for generating personalized recommendations.

Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies

Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.

The effect of neighborhood walkability on changes in physical activity and sedentary behavior during a 12-week pedometer-facilitated intervention

BACKGROUND

Pedometer-facilitated interventions encourage physical activity via the accumulation of steps. Mixed evidence suggests that neighborhood walkability might influence the effectiveness of physical activity interventions, including pedometer-facilitated interventions. Our study investigated the moderating effect of neighborhood walkability on immediate (4-week) and short-term (12-week) changes in self-reported neighborhood-specific leisure and transportation walking, leisure-based moderate and vigorous-intensity physical activity, and leisure-based screen time during a pedometer-facilitated intervention (UWALK).

METHODS

This quasi-experiment undertaken in Calgary (Canada) compared behavior changes during the 12-week intervention between two neighborhood groups classified as 'walkable' or 'car dependent' based on Walk Score®. Of the 573 volunteers (adults in the contemplation and preparation stages of physical activity behavior change), 466 participated in UWALK. Surveys captured sociodemographic characteristics, perceived neighborhood walkability, neighborhood preferences, motivation, physical activity and screen-based leisure. Covariate-adjusted linear mixed models estimated the differences in physical activity and leisure screen time between the neighborhood walkability groups at baseline, 4-weeks, and 12-weeks.

RESULTS

UWALK participants included mostly females (83%) and had an average age of 49.2 years. Weekly minutes of walking for transport inside the neighborhood was higher (p < .001) among participants from walkable versus car dependent neighborhoods at baseline (42.5 vs. 21.1), 4-weeks (81.2 vs. 48.2), and 12-weeks (87.2 vs. 48.0). Regardless of neighborhood walkability, all physical activity outcomes were higher and leisure screen time lower at 4-weeks and 12-weeks compared with baseline. We found no significant neighborhood group by time interactions.

CONCLUSIONS

Pedometer-facilitated interventions may be effective for supporting short-term changes in physical activity and sedentary behavior even among adults residing in low walkable neighborhoods.

Assessment Tools of Biopsychosocial Frailty Dimensions in Community-Dwelling Older Adults: A Narrative Review

Frailty is a complex interplay between several factors, including physiological changes in ageing, multimorbidities, malnutrition, living environment, genetics, and lifestyle. Early screening for frailty risk factors in community-dwelling older people allows for preventive interventions on the clinical and social determinants of frailty, which allows adverse events to be avoided. By conducting a narrative review of the literature employing the International Narrative Systematic Assessment tool, the authors aimed to develop an updated framework for the main measurement tools to assess frailty risks in older adults, paying attention to use in the community and primary care settings. This search focused on the biopsychosocial domains of frailty that are covered in the SUNFRAIL tool. The study selected 178 reviews (polypharmacy: 20; nutrition: 13; physical activity: 74; medical visits: 0; falls: 39; cognitive decline: 12; loneliness: 15; social support: 5; economic constraints: 0) published between January 2010 and December 2021. Within the selected reviews, 123 assessment tools were identified (polypharmacy: 15; nutrition: 15; physical activity: 25; medical visits: 0; falls: 26; cognitive decline: 18; loneliness: 9; social support: 15; economic constraints: 0). The narrative review allowed us to evaluate assessment tools of frailty domains to be adopted for multidimensional health promotion and prevention interventions in community and primary care.

Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation

Leading a sedentary lifestyle may cause numerous health problems. Therefore, passive lifestyle changes should be given priority to avoid severe long-term damage. Automatic health coaching system may help people manage a healthy lifestyle with continuous health state monitoring and personalized recommendation generation with machine learning (ML). This study proposes a semantic ontology model to annotate the ML-prediction outcomes and personal preferences to conceptualize personalized recommendation generation with a hybrid approach. We use a transfer learning approach to improve ML model training and its performance, and an incremental learning approach to handle daily growing data and fit them into the ML models. Furthermore, we propose a personalized activity recommendation algorithm for a healthy lifestyle by combining transfer learning, incremental learning, the proposed semantic ontology model, and personal preference data. For the overall experiment, we use public and private activity datasets collected from healthy adults (n = 33 for public datasets; n = 16 for private datasets). The standard ML algorithms have been used to investigate the possibility of classifying daily physical activity levels into the following activity classes: sedentary (0), low active (1), active (2), highly active (3), and rigorous active (4). The daily step count, low physical activity, medium physical activity, and vigorous physical activity serve as input for the classification models. We first use publicly available Fitbit datasets to build the initial classification models. Subsequently, we re-use the pre-trained ML classifiers on the real-time MOX2-5 dataset using transfer learning. We test several standard algorithms and select the best-performing model with optimized configuration for our use case by empirical testing. We find that DecisionTreeClassifier with a criterion "entropy" outperforms other ML classifiers with a mean accuracy score of 97.50% (F1 = 97.00, precision = 97.00, recall = 98.00, MCC = 96.78) and 96.10% (F1 = 96.00, precision = 96.00, recall = 96.00, MCC = 96.10) in Fitbit and MOX2-5 datasets, respectively. Using transfer learning, the DecisionTreeClassifier with a criterion "entropy" outperforms other classifiers with a mean accuracy score of 97.99% (F1 = 98.00, precision = 98.00, recall = 98.00, MCC = 96.79). Therefore, the transfer learning approach improves the machine learning model performance by ≈ 1.98% for defined datasets and settings on MOX2-5 datasets. The Hermit reasoner outperforms other reasoners with an average reasoning time of 1.1-2.1 s, under defined settings in our proposed ontology model. Our proposed algorithm for personalized recommendations conceptualizes a direction to combine the classification results and personal preferences in an ontology for activity eCoaching. The proposed method of combining machine learning technology with semantic rules is an invaluable asset in personalized recommendation generation. Moreover, the semantic rules in the knowledge base and SPARQL (SPARQL Protocol and RDF Query Language) query processing in the query engine helps to understand the logic behind the personalized recommendation generation.

Technology-based contingency management for walking to prevent prolonged periods of workday sitting

Sedentary behavior is an emerging public health issue. Frequent, brief bouts of walking are recommended by experts to reduce the health risks correlated with physical inactivity and prolonged sedentary periods. The purpose of the current study was to extend the literature by evaluating a remote, technology-based contingency management (CM) intervention that reinforced frequent, brief bouts of walking to decrease prolonged periods of sitting during the workday. A packaged intervention consisting of a contingency contract, monetary incentives, goal setting, textual prompts, and performance feedback was implemented with individuals with sedentary job responsibilities working from home during the COVID-19 pandemic. The intervention increased the number of physically active intervals to mastery for 4 participants, thereby disrupting prolonged periods of sedentary time. For 2 participants, the intervention did not meaningfully increase the number of physically active intervals. Results suggest that a remote, digital CM intervention can decrease sedentary behavior in home office environments.

Effectiveness of physical activity monitors in adults: systematic review and meta-analysis

OBJECTIVE

To estimate the effectiveness of physical activity monitor (PAM) based interventions among adults and explore reasons for the heterogeneity.

DESIGN

Systematic review and meta-analysis.

STUDY SELECTION

The electronic databases MEDLINE, Embase, SPORTDiscus, CINAHL, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched on 4 June 2021. Eligible randomised controlled trials compared interventions in which adults received feedback from PAMs with control interventions in which no feedback was provided. No restrictions on type of outcome measurement, publication date, or language were applied.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently extracted data and assessed risk of bias. Random effects meta-analyses were used to synthesise the results. The certainty of evidence was rated by the Grading of Recommendations Assessment and Evaluation (GRADE) approach.

MAIN OUTCOME MEASURES

The three primary outcomes of interest were physical activity, moderate to vigorous physical activity, and sedentary time.

RESULTS

121 randomised controlled trials with 141 study comparisons, including 16 743 participants, were included. The PAM based interventions showed a moderate effect (standardised mean difference 0.42, 95% confidence interval 0.28 to 0.55) on physical activity, equivalent to 1235 daily steps; a small effect (0.23, 0.16 to 0.30) on moderate to vigorous physical activity, equivalent to 48.5 weekly minutes; and a small insignificant effect (-0.12, -0.25 to 0.01) on sedentary time, equal to 9.9 daily minutes. All outcomes favoured the PAM interventions.

CONCLUSIONS

The certainty of evidence was low for the effect of PAM based interventions on physical activity and moderate for moderate to vigorous physical activity and sedentary time. PAM based interventions are safe and effectively increase physical activity and moderate to vigorous physical activity. The effect on physical activity and moderate to vigorous physical activity is well established but might be overestimated owing to publication bias.

STUDY REGISTRATION

PROSPERO CRD42018102719.

Sedentary Behavior Counseling Intervention in Aging People With Type 2 Diabetes: A Feasibility Study

This study examined the feasibility and effect of sedentary behavior (SB) counseling on total sitting time (TST) and glycemic control in people with type 2 diabetes (T2D). Community-dwelling sedentary adults with T2D (n = 10; 8 women; age 65.6 ± 7.31) completed SB counseling (motivational interviewing-informed education about SB) aided by an activity monitor with a vibrotactile feature (activPAL3TM). The monitor was worn for 7 days, on weeks 1 and 13 (without the vibrotactile feature) and during weeks 5 and 9 (with the vibrotactile feature). Intervention feasibility was determined by study retention rates and activity monitor tolerability, and differences between pre- and post-intervention average daily TST. Paired t-test were performed. The effect size (ES) was calculated using Cohen d. All participants attended all study sessions with only 20% reporting moderate issues tolerating the activity monitor. TST time decreased from 11.8 hours ± 1.76 at baseline to 10.29 hours ± 1.84 at 3 months’ assessment (P < .05) with a large ES (Cohen d = .88). HbA1c was decreased by 0.51% (P < .05) at the end of the intervention. This study found that the intervention was feasible for sedentary adults with type 2 diabetes.

Two-year follow-up after a six-week high-intensity training intervention study with breast cancer patients: physiological, psychological and immunological differences

PURPOSE

Previously we demonstrated the feasibility of a six-week-long combination of high-intensity interval endurance and strength training (HIT/HIRT) for women with nonmetastatic breast cancer leading to improvements in psychological well-being and performance. Now we report results of a 24-month follow-up.

METHODS

Previous intervention (IG, n = 10; 58.7 ± 8.4yrs) and control group (CG, n = 9; 58.8 ± 6.6yrs) were asked for follow-up examinations 12 (T12) and 24 months (T24) after cessation of the supervised training (POST). Medical history, mental well-being, performance and immunological variables were analyzed with respect to intervention start (PRE).

RESULTS

IG maximum oxygen consumption (⩒O_2peak) 12%-improved POST (p = 0.05) and declined to baseline values T24, while CG ⩒O_2peak increased 12% T24 (p = 0.01). IG strength (1RM) increased 31% POST (p < 0.001) and remained above baseline level T24 (p = 0.003), whereas CG 1RM slightly improved T24 (+19%, p = 0.034). IG Anxiety and Depression decreased POST and did not change until T24. IG C-reactive protein decreased POST and increased to pre-exercise levels T24. CG immunological/inflammatory/life quality markers did not change.

CONCLUSIONS

Six weeks of HIT/HIRT by breast cancer patients can induce similar beneficial effects like two years of convalescence, but outcomes were unstable and showed a fast backslide in aerobic capacity, activity level and in pro-inflammatory state within 12 months.IMPLICATIONS FOR REHABILITATIONHigh-intensity interval endurance and strength training (HIT/HIRT) for female breast cancer patients was shown to improve psychological well-being and performance, but long-term effects/adherence are unknown.Significant backslides in aerobic capacity, activity level as well as in the pro-inflammatory response after one and two years are observed and should be monitored.Continuous supervision and/or support of breast cancer patients before, during, and after medical care due to poor training adherence when voluntarily executed is recommended.

A systematic review and meta-analysis of interventions to reduce sedentary behavior among older adults

Sedentary behavior (SB) is associated with numerous negative health outcomes, independent of physical activity behavior. Older adults are the most sedentary population in the United States. Understanding the effects and characteristics of existing interventions to reduce SB can inform practice, future research, and public health initiatives to improve older adults’ health. We conducted a systematic review and meta-analysis to examine existing SB intervention research among older adults and quantitatively synthesize intervention effects. Comprehensive searches were conducted to identify studies testing interventions to reduce SB time among adults at least 60 years old. Data on study design, intervention content and delivery, and participant characteristics were extracted from eligible studies. Standardized mean difference effect sizes (Cohen’s d) were synthesized using a random-effects model for two-group pretest–posttest design studies. Twenty-two reports describing 17 distinct studies were included in the narrative synthesis, with eight studies included in the meta-analysis (k = 8; n = 1,024). Most interventions were theory-driven and employed multiple strategies, including education, self-monitoring, and goal setting. Although SB interventions significantly reduced total sedentary time, the overall effect was small (d = −0.25, 95% confidence interval [−0.50, 0.00], p = .05). Studies were significantly heterogeneous (Q = 22.34, p &lt; .01); however, the small number of comparisons prevented moderator analyses. Practitioners should employ diverse SB-specific strategies to encourage older adults to reduce time spent sedentary. To develop public health programs targeting SB in older adults, future research should include measures of time spent in specific SB and duration/number of breaks in sedentary time and investigate SB intervention effects on health outcomes.

Physical activity monitors to enhance amount of physical activity in older adults - a systematic review and meta-analysis

Background

The body of evidence related to the effect of physical activity monitor-based interventions has grown over the recent years. However, the effect of physical activity monitor-based interventions in older adults remains unclear and should be systematically reviewed.

Objective

The objective of this systematic review was to estimate the effect of physical activity monitor-based interventions on physical activity behavior in participants aged 65 and above. Subsequently we explored the effect on body mass index, physical capacity, and health-related quality of life and finally the impact of patient- and intervention characteristics.

Methods

Searches in MEDLINE, EMBASE, SPORTDiscus, CINAHL, and CENTRAL were performed on April 26, 2018. No publication date filters were applied. References of eligible studies were scrutinized and relevant journals were hand-searched. Randomized controlled trials and randomized cross-over trials investigating the effect of a physical activity monitor-based intervention on physical activity were included. Studies were included if the mean age of the participants was above 65 years, and participants could walk independently with or without walking aids. The Cochrane handbook was used as a template for extracting data and the RoB 2.0 tool was used to assess risk of bias. Random-effects meta-analysis using Hedges g, were used to pool the study results. The main outcome of this study was physical activity.

Results

Twenty-one studies with 2783 participants were included. The median participant age in the studies was 70.5 years, the median percentage of male participants was 42%, and the median baseline daily step count was 5268. Physical activity monitor-based interventions had a moderate effect (SMD = 0.54, 95% CI: 0.34 to 0.73) compared to control interventions, corresponding to an average increase of 1297 steps per day in the intervention groups. No impact of patient and intervention characteristics on the effect estimates were found.

Short conclusion

Low quality of evidence was found for a moderate effect of physical activity monitor-based interventions on physical activity compared with control interventions. More studies with higher research methodology standards are required.

PROSPERO registration

CRD42018083648.

Electronic supplementary material

The online version of this article (10.1186/s11556-019-0213-6) contains supplementary material, which is available to authorized users.

Feedback from physical activity monitors to enhance amount of physical activity in adults-a protocol for a systematic review and meta-analysis

OBJECTIVE

The use of physical activity monitors (PAMs) in the adult population is increasing rapidly and previous systematic reviews are outdated. The objective of this systematic review and meta-analysis is to estimate the effect of PAM-based interventions on physical activity behavior in adults. Furthermore, the impact of intervention, study, and participants characteristics will be investigated.

METHODS AND DESIGN

Randomized controlled trials and randomized cross-over trials including adult participants will be included if the study compares any PAM-based intervention where the participants receive feedback on their physical activity level measured by PAMs, to control interventions where participants do not receive feedback from PAMs. This protocol is detailed according to the recommendations of the Cochrane Handbook, and it is reported according to the preferred reporting items for systematic reviews and meta-analyses protocols statement. The results from the literature search will be presented in a PRISMA flow chart. The effects from individual studies will be summarized in a random effects meta-analysis and the impact of diagnosis of the participants, type of feedback, type of intervention, and control intervention will be investigated in stratified meta-analysis and meta-regressions analysis. The results on daily physical activity, moderate to vigorous physical activity, sedentary time, and adverse events will be presented in a summary of findings table.

DISCUSSION

The results will be useful to researchers, policy makers, and health care professionals when the intention is to increase physical activity in the adult population.

PROSPERO REGISTRATION

CRD42018102719.

Physical activity monitors to enhance the daily amount of physical activity in elderly-a protocol for a systematic review and meta-analysis

BACKGROUND

To investigate the use of physical activity monitors (PAMs) for the elderly, the scientific literature should be systematically reviewed and the effect quantified, as the evidence seems inconclusive.

METHODS AND DESIGN

Randomized controlled trials and randomized crossover trials, with participants with a mean age above 65 years, comparing any PAM intervention with other control interventions or no intervention, will be included. This protocol is detailed according to the recommendations of the Cochrane Handbook, and it is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols statement.

RESULTS

We will present results from the search in a flow diagram. The results from the analyses will include regular meta-analyses, stratified analyses, and meta-regressions. The results on each outcome of interest will be presented in a summary of findings table.

DISCUSSION

This paper will explore and analyze the heterogeneity of the results and try to identify variables that will enhance the effect of PAMs in elderly. The results will be useful to researchers working with elderly and/or PAMs, health care professionals working with elderly, and relatives together with the elderly themselves.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42018083648 .

The influence of a consumer-wearable activity tracker on sedentary time and prolonged sedentary bouts: secondary analysis of a randomized controlled trial

Objective

A recent meta-analysis surmised pedometers were a useful panacea to independently reduce sedentary time (ST). To further test and expand on this deduction, we analyzed the ability of a consumer-wearable activity tracker to reduce ST and prolonged sedentary bouts (PSB). We originally conducted a 12-month randomized control trial where 800 employees from 13 organizations were assigned to control, activity tracker, or one of two activity tracker plus incentive groups designed to increase step count. The primary outcome was accelerometer measured moderate-to-vigorous physical activity.

Results

We conducted a secondary analysis on accelerometer measured daily ST and PSB bouts. A general linear mixed model was used to examine changes in ST and prolonged sedentary bouts, followed by between-group pairwise comparisons. Regression analyses were conducted to examine the association of changes in step counts with ST and PSB. The changes in ST and PSB were not statistically significant and not different between the groups (P < 0.05). Increases in step counts were concomitantly associated with decreases in ST and PSB, regardless of intervention (P < 0.05). Caution should be taken when considering consumer-wearable activity trackers as a means to reduce sedentary behavior.

Trial registration NCT01855776 Registered: August 8, 2012

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3306-9) contains supplementary material, which is available to authorized users.

Personalized Physical Activity Coaching: A Machine Learning Approach

Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88-0.99, and mean F1-score = 0.90, range = 0.87-0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.

Television viewing time as a risk factor for frailty and functional limitations in older adults: results from 2 European prospective cohorts

Background

Sedentariness is an important risk factor for poor health. The main objective of this work was to examine the prospective association between television viewing time and indicators of physical function, mobility, agility, and frailty.

Methods

Data came from two independent cohorts of community-dwelling older adults: the Seniors-ENRICA (n = 2392, 3.5 year follow-up), and the ELSA (n = 3989, 3.9 year follow-up). At baseline, television viewing and other sedentary behaviors were ascertained using interviewer-administered questionnaires. In the Seniors-ENRICA cohort overall physical function at baseline and follow-up was assessed using the physical component summary (PCS) of the SF-12 Health Survey. Measures for incident mobility and agility limitations in both cohorts were based on standardized questions, and incident frailty was measured with the Fried criteria. Analyses were adjusted for the main confounders, including physical activity at baseline. Results across cohorts were pooled using a random effects model.

Results

Lower (worse) scores in the PCS were observed among those in the highest (vs. the lowest) tertile of television viewing time (b-coefficient:-1.66; 95% confidence interval:-2.81,-0.52; p-trend = 0.01). Moreover, the pooled odds ratios (95% CIs) for mobility limitations for the second and third (vs. the lowest) tertile of television viewing were 1.00 (0.84, 1.20) and 1.17 (1.00, 1.38); p-trend = 0.12, respectively. The corresponding results for agility limitations were 1.18 (0.97, 1.44) and 1.25 (1.03, 1.51); p-trend = 0.02. Results for incident frailty were 1.10 (0.80, 1.51) and 1.47 (1.09, 1.97); p-trend = 0.03. No association between other types of sedentary behavior (time seated at the computer, while commuting, lying in the sun, listening to music/reading, internet use) and risk of functional limitations was found.

Conclusions

Among older adults, longer television viewing time is prospectively associated with limitations in physical function independently of physical activity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12966-017-0511-1) contains supplementary material, which is available to authorized users.

Pedometer intervention and weight loss in overweight and obese adults with Type 2 diabetes: a meta-analysis

AIM

Although pedometer intervention is effective in increasing physical activity among adults with Type 2 diabetes, its impact on weight loss remains unclear. This meta-analysis was aimed to assess whether pedometer intervention promotes weight loss.

METHODS

Three different databases were searched for randomized controlled trials (RCTs) published in English up to April 2015. Studies were included if they investigated the effects of pedometer intervention on weight loss, as measured by BMI or weight. Effect sizes were aggregated using a random-effects model. Subgroup and meta-regression analyses were used to identify potential moderators. Eleven RCTs with 1258 participants were included. All enrolled participants were overweight or obese.

RESULTS

Pedometer intervention led to significantly decreased BMI [weighted mean difference (WMD) -0.15 kg/m(2) , 95% confidence interval (CI) -0.29 to -0.02 kg/m(2) ] and reduced weight (WMD -0.65 kg, 95% CI -1.12 to -0.17 kg). Dietary counselling seemed to be a key predictor of the observed changes. However, none of the following variables had a significant influence: step goal setting, baseline age, BMI, weight, sex distribution, disease duration, intervention duration, and baseline values or change scores for total or moderate-to-vigorous physical activity. After completion of the pedometer intervention, non-significant declines in BMI and weight were observed during the follow-up periods.

CONCLUSIONS

Pedometer intervention promotes modest weight loss, but its association with physical activity requires further clarification. Future studies are also required to document dietary and sedentary behaviour changes to facilitate the use of pedometers for weight loss in overweight and obese adults with Type 2 diabetes.