Raija Laukkanen

A controlled trial of the health benefits of regular walking on a golf course

PURPOSEnTo study the effects of regular walking during a golf game on various health and fitness indicators in middle-aged men.nnnMETHODSnStudy subjects were 55 healthy male golfers aged 48 to 64 years who had been sedentary during the 7 months before the study, and 55 age-matched, similarly sedentary controls. During the 20-week study, those in the intervention group were encouraged to play golf two to three times a week; the controls were not. Measurements of body composition, cardiorespiratory performance, motor and musculoskeletal fitness, blood pressure, and serum lipid, glucose, and insulin levels were obtained at baseline and after the 20-week study.nnnRESULTSnWalking during a golf game was a practical and safe form of physical activity with high adherence. It significantly increased aerobic performance and trunk muscle endurance, with a net difference (pretraining to posttraining change between the golfers and controls) of 36 seconds (95% confidence interval [CI]: 19 to 53 seconds, P < 0.001) for treadmill walking time and 13 seconds (95% CI: 2 to 24 seconds, P = 0.02) for static back extension. In addition, regular walking favorably affected body composition, including reductions in weight of 1.4 kg (95% CI: 0.6 to 2.1 kg, P < 0.001), in waist circumference of 2.2 cm (95% CI: 1.0 to 3.3 cm, P < 0.001), and in abdominal skin fold thickness of 2.2 cm (95% CI: 0.9 to 3.4 cm, P = 0.001). Golfers also had significantly greater increases in serum high-density lipoprotein (HDL) cholesterol levels and in the ratio of HDL cholesterol to total cholesterol.nnnCONCLUSIONSnRegular walking had many positive effects on the health and fitness of sedentary middle-aged men. Walking during a golf game is characterized by high adherence and low risk of injury and is therefore a good form of health-enhancing physical activity.

Assessment of energy expenditure in overweight women.

PURPOSEnTo compare field measures of average daily energy expenditure (ADEE) against criterion data by the doubly labeled water method (DLW) in overweight women.nnnMETHODSnThe subject were 20 overweight (BMI 29.9 +a- 3.0 kg.m-2) premenopausal women. Energy expenditure was measured by DLW and by the factorial method (activity diary, two techniques differing by method to obtain resting energy expenditure, REE), heart-rate monitoring (HR, two techniques differing by the FLEX-point to discriminate sedentary and activity HR), accelerometer, and pedometer.nnnRESULTSnThe ADEE(DLW) was 10.26 +a- 1.1 MJ.d-1. The mean bias (ADEE by the alternative minus ADEE(DLW) was smallest for the accelerometer (+ 0.08 +a- 1.63 MJ) and HR-FLEX10 (+ 0.11 +a- 1.67 MJ). The HR-FLEX(0) technique (lower FLEX-point) overestimated ADEE by + 1.18 (+a- 1.97 MJ). However, the random error (SD of bias) was smaller for both factorial techniques (REE measured: -0.48 +2- 0.81 MJ; REE calculated from the WHO equation: -0.22 +2- 0.88 MJ).nnnCONCLUSIONnThe results show that simple factorial methods may assess ADEE with small random errors in population with a rather narrow range of physical activity. The accelerometer and HR with the higher FLEX-point have comparable results with smaller bias but larger random error compared with the factorial techniques.

Weight Loss Strategies for Obese Adults: Personalized Weight Management Program vs. Standard Care

Objective: The objective of this study was to evaluate the effect of a 32‐week personalized Polar weight management program (PWMP) compared with standard care (SC) on body weight, body composition, waist circumference, and cardiorespiratory fitness in overweight or obese adults.

Polar Activity Watch 200: a new device to accurately assess energy expenditure

Objectives Energy expenditure (EE) based on movement detection is calculated by a new device, the Activity Watch 200 (AW200). The aim of this study was to validate EE measured by this device against indirect calorimetry (IC) and to assess the reproducibility of AW200 measurements. Design EE was assessed during a 9.7 km hike. 10 men and 10 women in the age range 35–45 years, and 5 men and 6 women in the age range 50–55 years were tested. One in five participants of each age- and sex-matched group was equipped with a portable metabograph (Oxycon Mobil) for IC measurements. Data were collected every 30 min during the hike, and IC was extrapolated for the remaining four other participants of the group. Results During the total hike, there was a high correlation between EE obtained from the AW200 and the IC calculation (r = 0.987, p<0.001). Identical values of EE were calculated by both methods during the first 90 min of the hike. However, EE calculated by the AW200 at 120 min and at the end of the hike was lower (p<0.05). Bland–Altman analysis showed limits of agreements between 105 and 279 kJ after 30 and 120 min, respectively. EE measured by the AW200 was well correlated with IC measurements, and limits of agreement between devices were below 10% of the measured values for hike durations longer than 60 min. Conclusion The AW200 appears to be a very useful and accurate device for measuring EE during exercise in recreational hikers and provides a useful tool for keeping track of personal EE.

A Cluster Randomized Controlled Trial on the Effects of Technology-aided Testing and Feedback on Physical Activity and Biological Age Among Employees in a Medium-sized Enterprise

Background It has been suggested that engaging technology can empower individuals to be more proactive about their health and reduce their health risks. The aim of the present intervention was to study the effects of technology-aided testing and feedback on physical activity and biological age of employees in a middle-sized enterprise. Methods In all, 121 employees (mean age 42 ± 10 years) participated in the 12-month three-arm cluster randomized trial. The fitness measurement process (Body Age) determined the participants’ biological age in years. Physical activity was measured with the International Physical Activity Questionnaire Short Form. Results Physical activity did not change during the intervention. Biological age (better fitness) improved in all groups statistically significantly (p < 0.001), but with no interaction effects. The mean changes (years) in the groups were −2.20 for the controls, –2.83 for the group receiving their biological age and feedback, and −2.31 for the group receiving their biological age, feedback, and a training computer. Conclusion Technology-aided testing with feedback does not seem to change the amount of physical activity but may enhance physical fitness measured by biological age.