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. 2020 Aug 13;15(8):e0237495. doi: 10.1371/journal.pone.0237495

Levels of and determinants for physical activity and physical inactivity in a group of healthy elderly people in Germany: Baseline results of the MOVING-study

Fabian Kleinke 1,2,*, Peter Penndorf 1,2, Sabina Ulbricht 2,3, Marcus Dörr 2,4, Wolfgang Hoffmann 1,2, Neeltje van den Berg 1,2
Editor: Thalia Fernandez5
PMCID: PMC7425978  PMID: 32790711

Abstract

Background

Low levels of physical activity (PA) and high levels of physical inactivity (PI) are associated with higher mortality and cardiovascular diseases. Higher age is associated with a decrease of PA, only 2.4–29% of ≥60 year-olds achieve the PA times recommended by WHO. The aim of this study was to identify levels of and determinants for moderate PA, overall PA and PI in a sample of individuals aged ≥65 years.

Methods

We analyzed baseline data from an intervention-study aiming to increase PA and decrease PI by automatically generated feedback letters to objectively measured PA and PI. Recruitment was multimodal including re-contacting participants of previous studies and advertisements in regional public buses and newspapers. At baseline, participants wore an accelerometer over a period of 7 consecutive days. PA was categorized using cut-points suggested by Freedsoon 1998 in light, moderate and vigorous physical intensity as well as physical inactivity. Potential determinants (self-efficacy, education) were measured by questionnaires or in a physical examination (BMI). Multiple linear regression models were fitted to identify determinants for PA and PI.

Results

N = 199 persons (mean age 71.0 years (SD 4.9), 59.3% female) participated in the study. The weekly amount of overall PA for men was on average 1,821 minutes (SD 479.1), for women on average 1,929 minutes (SD 448.8). 79.7% of the women and 72.8% of the men achieved the WHO recommendation of 30 minutes moderate PA/day at baseline. The time of PI during the observation time period of 7 days was on average 4,057 minutes in men and 3,973 minutes in women. In males, age was found to be a significant negative determinant for overall PA (p = 0.002) and for moderate PA (p<0.001). Higher education was positively associated with higher levels of overall PA (p = 0.013) and moderate PA (p = 0.06) in men. BMI was a significant negative determinant for overall PA both in men (p = 0.039) and women (p = 0.032) as well as for moderate PA for women (p = 0.009). Only in women, not in men, self-efficacy was to be a significant positive determinant for overall PA (p = 0.020) as well as negatively associated with PI (p = 0.006).

Discussion

The participants of our study showed high levels of PA. This is likely due to selection bias in this convenience sample. However, also levels of PI are very high and those correspond with average levels in the German population. The determinants for higher PA and lower PI differed between males and females. Thus, strategies for improving PA and decrease PI are likely different with respect to sex and should take individual factors (e.g. age, BMI) into account.

Trial registration number

DRKS00010410 Date: 17 May 2017

Introduction

Physical activity (PA) is defined as “any bodily movement produced by skeletal muscles that requires energy expenditure” [1]. There is strong evidence, that regular PA is a very effective, nonpharmacological and noninvasive health-promoting method [2–4]. A PA-promoting lifestyle is associated with a reduced risk of mortality and is correlated with improved overall health status [3]. Additionally, high levels of PA significantly decreases overall mortality by 22–34% and CVD mortality by 27–35% [5]. Recent reviews showed that regular PA is also associated with a 18–28% reduced risk of developing dementia in older adults [6, 7] and is considered as one of the proxies of the concept of cognitive reserve [8]. One of the two most significant modifiable risk factors for dementia is PI. Additionally, PA (in particular aerobic exercise) is positively associated with a less age-related reduction of organic brain matter [9].

International PA guidelines recommend for healthy individuals aged over 65 years at least 150 minutes/week of moderate PA or at least 75 minutes/week of vigorous PA, or an equivalent combination of weekly PA. In fact, there is no difference to recommendations for healthy younger individuals aged 18–64 years. PA should be performed in uninterrupted time periods (bouts) of a duration of at least 10 minutes [10, 11].

Data from the United Kingdom show that 19% of men and 14% of women aged 65–74 years reach the recommended level of PA [2]. A study from Norway showed that 29% of men and 25% of women aged 65–69 years meet the recommendations. In the age group 80–85 years only 7.1% of women and 3% of men reach the WHO-recommendations [12]. In the USA (National Health and Nutrition Survey, NHANES) the proportion of people aged over 60 years who achieved the recommended amount of PA was 2.4% [13]. In Germany, only 19.3% of men and 16.8% of women aged 60–69 years achieve the WHO recommendations regarding PA (self-reported via questionnaire). In the age group 70–79 years, this proportion declines to 16.5% in men and to 11.0% in women [14].

In Europe in 2015, 17,4% of the total population was 65 years or older [15]. In Germany, 21.2% of the population was 65 years or older in 2017 [16]. This proportion will further increase to 33% until 2060 [17]. Older age is associated with an higher risk for chronic diseases, multimorbidity [18–20]. Prevalences of CVD, including coronary heart disease (CHD) and stroke [21] will increase.

Low PA is one of the 10 leading risk factors for global mortality. Globally, 31.1% of the adults are insufficiently physically active [22]. A reduction of the prevalence of insufficient PA is a global target of the WHO [23, 24]. Physical inactivity (PI), defined “as any waking behaviour characterized by an energy expenditure ≤1.5 METs (metabolic equivalent of task) while in a sitting or reclining posture” (Sedentary Behaviour Research Network, 2012; Tremblay et al., 2017) causes 3.2 million deaths per year worldwide, and in 2010 was estimated to be responsible for 69.3 million DALYs (disability-adjusted life years) globally [23]. Besides low levels of PA, also PI is a crucial risk factor for mortality [14, 25, 26]. Frequently interrupted PI is associated with positive effects on health status and a reduced risk for premature death [27]. Lack of PA and a high level of PI are associated with an array of non-communicable diseases (NCD) and e.g. responsible for approximately 21–25% of breast and colon cancers, 27% of diabetes cases and approximately 30% of ischaemic heart disease (prevalences)(World Health Organization, 2018). Additionally, it seems, that physical inactivity is an important preventable factor for Alzheimer’s dementia [28].

Older people spent on average 8 to 9 hours a day sedentary which correspondents with 65–80% of their waking time [29]. Depending on the exact definition, the distrubution of PI across European countries ranged from 43.3% in Schweden up to 87.8% in Portugal. In Germany, 70.2% of men and 71.8% of women showed a sedentary lifestyle (low energy expenditure: <10% of the leisure-time expenditure in activities requiring ≥4 metabolic equivalents (MET) [30].

The amount of PA and PI depends on individual factors such as age, BMI, gender, education, social status and self-efficacy [31–33]. Additionally, environmental and policy factors [31, 33], weather conditions, and length of the day have an effect on the amount of PA of people [34, 35].

Overweight and obesity (BMI>30 kg/m2) have a negative influence on the level of PA and PI in the elderly. In a cross-sectional study, in which 15,239 subjects were surveyed from 15 member states of the European Union it was found, that people with a low BMI (<20 kg/m2) and normal BMI (20–25 kg/m2) have low prevalence in PI (both genders). In contrast, people with a BMI above 30 kg/m2 showed a more sedentary lifestyle [30, 36].

Higher education has a positive influence on PA and PI. Varo et al (EU study) showed, that people with primary level education were more sedentary than participants with higher levels of education (greater difference in women) [30].

Further factors that influence the amount of PA and PI are marital status, income, wellbeing, psychosocial variables such as self-efficacy, and social and cultural parameters [30, 31, 36, 37].

A report from the WHO about the prevention of non-communicable-diseases (NCD) in south-eastern European countries showed that the promotion of PA and reduction of PI are key aspects in public health efforts. Promoting physical activity through mass media was a primary goal for immediate action [38]. In addition, the Global Strategy on Diet, Physical Activity on Health [25] and the European Charter on Counteracting Obesity [39] underline the relevance of PA to fight against obesity.

To develop effective prevention strategies, adapted to the elderly, detailed information on the levels of PA and PI and their determinants are necessary. In this analysis, we assessed the levels of PA and PI and identified positive and negative determinants for PA and PI in a sample of healthy people aged ≥ 65 years.

Materials and methods

The data for this analysis were retrieved from the baseline assessment of an intervention study with the goal to increase PA and reduce PI in elderly people with a low-threshold intervention (MOVING–Motivation oriented intervention study for the elderly in Greifswald) [40]. This study is a two-arm, randomized controlled trial consisting of assessment of eligibility, baseline examination, randomization, intervention (only the participants in the intervention group), and follow-up examinations at 3, 6, and 12 months after baseline. The study region was Western Pomerania, a rural area in the Northeast of Germany.

Study participants in the intervention group received two individual feedback letters containing objectively measured PA and PI times based on data from the accelerometer device captured at baseline and 3 months follow-up measurement. Feedback-letters were automatically generated in R software (version 3.3.2 or later, Lucent Technologies, Murray Hill, NJ, USA).

A comprehensive description of the study protocol for the MOVING study is published elsewhere [40].

Inclusion of the participants

The study participants had to meet the following inclusion criteria:

  • Age ≥ 65 years

  • Ability to be physically active in daily life

Exclusion criteria were:

  • Inability to walk independently (e.g. permanent use of a wheelchair)

  • Simultaneous participation in other studies addressing PA or PI

  • Not accessible by telephone or cell phone

  • Already fulfilling the WHO recommendations for PA (self-reported) for people ≥ 65 years at baseline (≥ 300 minutes moderate PA per week)

Recruitment was performed in 2017 in several ways:

  • Re-contacting participants of a previously performed study [41];

  • Recruitment at venues frequented by older people, e.g. senior sport hours in the public swimming pool, rehearsals of senior choirs, events at meeting places for elderly people;

  • Persons contacted the study centre after reading articles about the project in regional newspapers and television, advertisements in regional buses, and flyers and posters that were distributed in GP practices, hospitals and meeting centers for elderly people.

All participants were informed in detail about the study and had to give their written informed consent.

Measures

All study participants received a baseline examination consisting of the assessment of blood pressure, somatometry data (body weight, waist and hip circumference) as well as sociodemographics (sex, age, education, job and partnership status, general health status). The SSA scale (self-efficacy towards physical exercise) was used to asses the participants’ level of self-efficacy The result of that scale is a sum in which higher values indicate higher self-efficacy towards PA [42].

After that, the study participants received an accelerometer device (ActiGraph GT3x-BT, Pensacola, FL, USA) which captures and records PA and PI continuously at a sampling frequency of 30 Hz over a period of seven consecutive days, starting at midnight after the baseline examination. Participants were instructed to wear the accelerometer device for seven days only during daytime on the right hip and to remove it only at bedtime and for water-based activities (e.g. showering, swimming). In addition, all study participants were asked to document their physical activities in a semi-standardized protocol for each day of the observation time. Besides the objective assessment of PA and PI, the participants were instructed to answer paper-based questionnaires to assess self-reported PA and PI with regard to the observation period of the accelerometer.

To asses self-reported PA, the International Physical Activity Questionnaire short form (IPAQ-SF) in German version was used. The IPAQ consists of seven items, addressing intensity and duration of PA in daily life over the last 7 days by self-report [43]. In addition, the German Physical Activity Questionnaire for 50+ (German PAQ 50+) was used to assess type and duration of PA in daily life by self-report [44]. PI was assessed by the Measure of Older Adults’ Sedentary Time (MOST Questionnaire) in the German version [45].

Body mass index was calculated from measured body height and body weight (1 = < 25 kg/m2, 2 ≥ 25 kg/m2 and 3 = < 30 kg/m2, ≥ 30 kg/m2).

After seven days, the participants had to bring the accelerometer device and the fulfilled questionnaires back to the examination center.

Data analysis

Data assessment and documentation were conducted based on eCRFs in an IT-supported documentation system, which is characterized by an independent operation, synchronization and monitoring. The software is based on the concept of offline clients and each staff member of the DZHK had individual login data [46].

The paper-based questionnaires were documented using the software Cardiff Teleform® (Electric Paper, Lüneburg, Germany). All questionnaires and the daily physical activity protocol contained 1-D barcodes to ensure anonymization of the study participants.

The ActiLife software (versions 6.13.2 to 6.13.3, ActiGraph Cop. ©, Pensacola, FL, USA) was used to download the PA data from the accelerometer. The raw data were calculated into 10-second epochs and saved in raw format as GT3X files. A valid measurement day was defined as a record of at least 10 hours of total wearing time. Measurements of at least 4 valid days were required to be included into data analysis. To categorize PA intensity, we used specific cut points based on Freedson et al. [47]. PA intensity was divided into sedentary (0–99 counts), light (100–1951 counts), moderate (1952–5724 counts), and vigorous (5725–9498 counts) PA. Non-wearing time was defined as having ≥60 minutes of continual zero counts (range ≤2 minutes between 0 and 100 counts).

Descriptive statistics were used to describe the population with respect to the levels of PA and PI. To identify determinants of PA and PI, multiple linear regression models were calculated. Dependent variables for the regression models were overall and moderate PA as well as PI. The effect of the independent variables age, BMI, education and self-efficacy was examined for all regression models. All independent variables were checked for multicollinearity using a correlation (Pearson). To include education as a determinant in the multiple linear regression models we used dummy variables. Referring to the Gauß-Markov-Theorem we analyzed the residuals and requirements of multiple linear regression like distribution and homoscedasticity.

Statistical significance was assumed for p-values <0.05. Data processing and statistical calculations were performed with IBM SPSS Statistics 25 or later (1989–2018 by IBM Corp. ©, Armonk, New York, USA.). All statistical analysis were performed based on pseudonymized data. All identifying data were separated from the project data to the earliest possible timepoint. All appointments were ensured face-to-face by the study staff.

Ethics

The study was approved by the Ethics Committee of the University Medicine Greifswald (ethic approval protocol number BB071/16) and registered at the German Clinical Trials Register, ID: DRKS00010410.

Results

Of 258 screended study participants, 199 could be included in the analyses (Fig 1).

Fig 1. Number of recruited participants and participants included in the analysis.

Fig 1

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199 participants were included in the analysis, thereof 118 women (59.3%) and 81 men (40.7%). The mean age was 71.0 years (SD 4.9). At the baseline measurement of PA, participants had a mean wearing time of the accelerometer of 5,892.8 minutes (SD 766.6) per week which corresponds to an average of 14.0 hours/day (Table 1).

Table 1. Descriptive characteristics of the study sample (n = 199).

Characteristics n Mean (SD) or n (%)
Sex (women) 199 118 (59.3%)
Age (yr) 199 71.0 (SD 4.9)
Education (yr) 180
< 10 years 38 (20.2%)
= 10 years 50 (26.6%)
> 10 years 92 (48.9%)
Body mass index (kg/m2) 198
< 25 54 (27.3%)
≥ 25 - < 30 79 (39.9%)
≥ 30 65 (32.8%)
Currently Smoking (yes) 195 12 (6.2%)
Number of participants currently having a partnership (yes) 194 141 (71.9%)
Wearing time 199 5892.8 (SD 766.6)
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n number of subjects, SD standard deviation.

Overall, there were no significant difference between women and men according to overall PA (mean women: 1,929.4 minutes, mean men: 1,821.2 minutes per week, T(df = 197) = 1.626; p = 0.106). Men in higher age groups showed lower levels of weekly light PA. The mean number of minutes of moderate PA increased with higher education in men and in women. Both men and women with higher BMI showed lower levels of moderate and vigorous PA (Table 2).

Table 2. Mean duration of weekly PA [min.], without specific cut points, by intensity of PA and overall PA.

Mean duration of weekly [min.] light PA (95% CI) Mean duration of weekly [min.] moderate PA (95% CI) Mean duration of weekly [min.] vigorous PA (95% CI) Mean duration of weekly [min.] overall PA (95% CI)
Male Female Male Female Male Female Male Female
Age (yr)
65–69 (♂ = 32; ♀ = 66) 1602.2 (1461.0–1743.4) 1659.7 (1566.5–1753.0) 389.4 (324.3–454.5) 322.8 (281.4–364.2) 17.9 (3.5–39.2) 5.6 (3.0–8.3) 2009.7 (1825.0–2194.5) 1988.9 (1884.7–2093.0)
70–74 (♂ = 24; ♀ = 26) 1453.3 (1315.5–1591.0) 1627.7 (1502.2–1753.1) 272.9 (197.2–348.6) 252.8 (194.1–311.5) 6.8 (0.3–13.9) 4.8 (2.1–11.7) 1733.2 (1585.6–1880.9) 1887.9 (1736.1–2039.7)
75–79 (♂ = 19; ♀ = 20) 1511.5 (1318.3–1704.8) 1571.9 (1319.4–1824.4) 229.0 (150.1–307.9) 198.0 (133.6–262.4) 3.0 (0.4–5.6) 1.0 (0.7–1.3) 1743.9 (1510.6–1977.2) 1771.0 (1491.9–2050.2)
80+ (♂ = 6; ♀ = 6) 1317.8 (924.3–1711.2) 1654.8 (1211.0–2098.5) 92.1 (22.8–161.4) 322.1 (155.8–488.5) 2.4 (0.2–4.7) 6.3 (5.6–18.3) 1412.4 (1004.3–1820.6) 1983.4 (1561.5–2405.4)
Education (yr)
< 10 (♂ = 21; ♀ = 17) 1489.9 (1319.5–1660.3) 1691.7 (1445.3–1938.2) 218.7 (166.5–271.0) 192.0 (131.0–252.7) 8.8 (4.6–22.2) 1.5 (1.0–2.0) 1717.7 (1521.1–1914.3) 1885.3 (1622.8–2147.8)
= 10 (♂ = 15; ♀ = 35) 1359.3 (1219.8–1498.9) 1628.1 (1489.2–1767.0) 288.8 (159.6–418.1) 278.1 (231.6–324.6) 27.0 (17.7–71.8) 5.8 (2.5–9.1) 1675.6 (1470.3–1880.9) 1913.1 (1764.5–2061.8)
> 10 (♂ = 39; ♀ = 53) 1567.8 (1440.4–1695.2) 1632.3 (1529.2–1735.3) 316.3 (254.0–378.6) 317.6 (272.6–362.6) 5.3 (1.6–9.0) 5.8 (1.6–10.0) 1889.6 (1727.4–2051.7) 1957.1 (1832.4–2081.8)
BMI (kg/m2)
<25 (♂ = 13; ♀ = 41) 1625.6 (1313.9–1937.2) 1663.2 (1525.1–1801.2) 350.3 (213.2–487.5) 331.6 (282.6–380.5) 31.1 (21.2–83.3) 7.4 (3.3–11.5) 2007.0 (1646.7–2367.2) 2003.1 (1858.0–2148.3)
≥25 ─ <30 (♂ = 40; ♀ = 39) 1533.8 (1417.5–1650.0) 1698.4 (1580.0–1816.7) 274.7 (218.7–330.7) 315.3 (258.2–372.3) 4.4 (0.9–7.9) 4.4 (0.4–9.2) 1813.2 (1664.9–1961.6) 2019.9 (1883.3–2156.4)
≥30 (♂ = 27; ♀ = 38) 1435.3 (1308.3–1562.3) 1547.5 (1425.2–1669.7) 302.8 (225.5–380.3) 207.4 (163.9–250.8) 8.4 (1.9–18.7) 2.1 (1.5–2.7) 1746.7 (1571.5–1921.9) 1757.1 (1617.3–1893.8)
Currently having a partnership (yes)
Yes (♂ = 70; ♀ = 71) 1507.1 (1415.8–1598.3) 1680.3 (1586.2–1774.4) 283.3 (237.9–328.7) 294.1 (254.2–334.1) 10.1 (0.3–19.8) 5.9 (2.6–9.3) 1800.7 (1685.6–1915.8) 1981.9 (1880.3–2083.6)
No (♂ = 8; ♀ = 45) 1570.0 (1239.6–1900.3) 1561.0 (1443.1–1678.9) 356.4 (215.2–497.6) 266.1 (220.1–313.7) 12.2 (7.5–31.9) 2.9 (1.2–4.6) 1939.1 (1499.1–2379.1) 1830.9 (1689.3–1972.5)
Total (♂ = 81; ♀ = 118) 1515.7 (1432.4–1599.1) 1637.5 (1565.4–1709.7) 295.2 (253.3–337.2) 286.2 (256.3–316.1) 10.0 (1.4–18.5) 4.7 (2.6–6.8) 1821.2 (1715.3–1927.1) 1929.4 (1847.6–2011.2)
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M Mean, CI Confidence interval, min. minutes.

Male participants spent 68.9% of their waking time in PI, female participants 67.1%. Older age was associated with an increasing time of PI. In men, the level of PI increased with age (65–69 yr = 3837.3 minutes; 80+ = 4339.1 minutes) and with higher education (<10 yr = 3925.7 minutes; >10 yr = 4240.7 minutes). In women, the time of PI was largely independent of age (65–69 yr = 4,029.2 minutes; 80+ = 4,018.6 minutes) (Table 3).

Table 3. Mean duration of weekly PI [min.], proportion of sedentary time of total wake time in minutes.

Mean min. of weekly PI (95% CI) Mean proportion of daily PI (daytime) in % (95% CI)
Male Female Male Female
Age (yr)
65–69 (♂ = 32; ♀ = 66) 3837.3 (3633.9–4040.8) 4029.2 (3846.7–4211.8) 65.7% (62.8–68.5) 66.8% (65.1–68.4)
70–74 (♂ = 24; ♀ = 26) 4166.3 (3936.2–4396.5) 3766.2 (3514.7–4017.7) 70.6% (68.3–72.9) 66.4% (63.6–69.2)
75–79 (♂ = 19; ♀ = 20) 4202.4 (3736.7–4668.0) 4042.7 (3459.1–4626.2) 70.4% (66.4–74.3) 68.9% (63.7–74.1)
80+ (♂ = 6; ♀ = 6) 4339.1 (3502.7–5175.6) 4018.6 (3758.4–4278.7) 75.3% (68.9–81.8) 67.2% (61.9–72.4)
Education (yr)
< 10 (♂ = 21; ♀ = 17) 3925.7 (3646.8–4204.6) 3639.3 (2939.8–4338.8) 69.4% (65.8–73.0) 64.9% (59.4–70.4)
= 10 (♂ = 15; ♀ = 35) 4047.6 (3795.2–4300.0) 4112.8 (3908.9–4313.7) 70.8% (67.8–73.8) 68.3% (66.3–70.3)
> 10 (♂ = 39; ♀ = 53) 4240.7 (3986.3–4495.2) 3988.6 (3796.7–4180.5) 69.0% (66.5–71.6s) 67.0% (65.1–68.9)
BMI (kg/m2)
<25 (♂ = 13; ♀ = 41) 3905.5 (3559.6–4251.3) 3961.4 (3778.9–4143.9) 66.3% (61.4–71.2) 66.4% (64.2–68.7)
≥25 - <30 (♂ = 40; ♀ = 39) 4175.7 (3915.7–4435.7) 3862.1 (3506.4–4217.9) 69.5% (66.9–72.0) 65.0% (62.5–67.5)
≥30 (♂ = 27; ♀ = 38) 3979.5 (3756.6–4202.3) 4099.3 (3879.3–4319.4) 69.5% (66.8–72.3) 69.9% (67.6–72.2)
Currently having a partnership (yes)
Yes (♂ = 70; ♀ = 71) 4066.2 (3893.0–4239.3) 3873.9 (3696.8–4051.0) 69.2% (67.3–71.1) 65.9% (64.2–67.7)
No (♂ = 8; ♀ = 45) 4045.1 (3530.3–4559.9) 4123.9 (3853.0–4394.8) 67.7% (61.5–74.0) 69.0% (66.7–71.4)
Total (♂ = 81; ♀ = 118) 4057.6 (3902.4–4212.8) 3973.0 (3825.3–4120.7) 68.9% (67.2–70.6) 67.1% (65.7–68.5)
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CI Confidence interval, min. minutes.

N = 59 (72.8%) of the men and n = 94 (79.7%) of the women achieved the international recommendations for moderate PA (≥150 minutes moderate PA). The proportion of participants who fulfilled the recommendations decreased with age (Table 4).

Table 4. Number and proportion of study participants who fulfilled the WHO recommendations for moderate PA, separately for men and women.

Male Female
n (% of age group) % of all men n (% of age group) % of all women
Age (yr)
65–69 (n = 98) 29 of 32 (90.6%) 35.8% 56 of 66 (84.8%) 49.1%
70–74 (n = 50) 17 of 24 (70.8%) 21.0% 19 of 26 (73.1%) 16.7%
75–79 (n = 39) 12 of 19 (63.2%) 14.8% 13 of 20 (65.0%) 11.4%
80+ (n = 12) 1 of 6 (16.7%) 1.2% 6 of 6 (100%) 5.3%
Total (n = 199) 59 of 81 72.8% 94 of 118 79.7%
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n number of subjects, fulfillment of WHO recommendations for moderate PA for people aged 65 and older: ≥150 minutes moderate PA or ≥75 minutes vigorous PA or an equivalent combination per week.

A multiple linear regression was calculated to predict overall PA based on independent variables in Table 5. In men, lower age (p = 0.002), a lower BMI (p = 0.039) and higher education (p = 0.013) were found to be significant positive determinants for overall PA. Higher BMI is a negative determinant for overall PA in women (p = 0.032). Additionally, self-efficacy was found to be a significant positive determinant for overall PA (p = 0.02). The overall model fit was R2 = 0.23 for men and R2 = 0.11 for women (Table 5).

Table 5. Multiple linear regression model to identify determinants for overall PA, separately for men and women.

Predictors Male (n = 62, adj. R2 = 0.23) Female (n = 91, adj. R2 = 0.11)
β (95% CI) p β (95% CI) p
Intercept 5406.66 (3189.607–7623.706) <0.001** 3344.65 (1422.593–5266.706) 0.001**
Age -36.85 (-60.003 –-13.689) 0.002** -18.96 (-42.561–4.634) 0.114
BMI -31.10 (-60.537 –-1.685) 0.039* -21.39 (-40.863 –-1.912) 0.032*
Educationa
    <10 yr -257.34 (-538.976–24.298) 0.072 31.68 (-287.079–350.446) 0.844
    = 10 yr -400.08 (-712.358 –-87.801) 0.013* -56.61 (-285.765–172.546) 0.624
    >10 yr (“Abitur”, qualification for the university) 62.07 (-235.838–359.973) 0.678 -179.96 (-470.201–110.276) 0.221
    other -52.37 (-720.459–615.722) 0.876 -5.99 (-546.136–534.163) 0.982
Self-efficacy 1.35 (-7.230–9.926) 0.754 9.50 (1.552–17.438) 0.020*
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β regression coefficient, CI confidence interval

**p<0.01

*p<0.05

a reference: high school degree.

For moderate PA, higher age (p<0.001) was found to be a significant negative determinant in men. In women, a higher BMI was a significant negative determinant (p = 0.009). For male participants, lower education (<10 yr) was a significant negative determinant (p = 0.013) as well as 10 years of education (p = 0.006) (Table 6).

Table 6. Multiple linear regression model to identify determinants for moderate PA, separately for men and women.

Predictors Male (n = 62, adj. R2 = 0.30) Female (n = 91, adj. R2 = 0.19)
β (95% CI) p β (95% CI) p
Intercept 2076.30 (1235.748–2916.848) <0.001** 894.34 (266.772–1521.912) 0.006**
Age -19.73 (-28.505 –-10.946) <0.001** -6.09 (-13.793–1.616) 0.120
BMI -9.71 (-20.866–1.447) 0.087 -8.49 (-14.849 –-2.131) 0.009**
Educationa
    <10 yr -137.18 (-243.953 –-30.399) 0.013* -99.92 (-203.994–4.164) 0.060
    = 10 yr -168.82 (-287.215 –-50.427) 0.006** -48.98 (-123.801–25.842) 0.197
    >10 yr (“Abitur”, qualification for the university) -36.06 (-149.003–76.887) 0.525 -61.84 (-156.602–32.929) 0.198
    Other 54.59 (-198.699–307.887) 0.667 175.51 (-0.851–351.877) 0.051
Self-efficacy -0.34 (-3.590–2.914) 0.836 1.49 (-1.102–4.084) 0.256
Open in a new tab

β regression coefficient, CI confidence interval

**p<0.01

*p<0.05

a reference: high school degree.

In women, better self-efficacy was found to be a significant positive determinant (p = 0.006) for PI (Table 7).

Table 7. Multiple linear regression model to identify determinants for PI, separately for men and women.

Predictors Male (n = 62, adj. R2 = 0.03) Female (n = 91, adj. R2 = 0.14)
β (95% CI) p β (95% CI) p
Intercept 1222.26 (-2540.348–4984.866) 0.518 5684.80 (2881.732–8487.870) <0.001**
Age 36.92 (-2.380–76.220) 0.065 -16.49 (-50.903–17.924) 0.343
BMI 6.18 (-43.762–56.117) 0.805 13.05 (-15.355–41.451) 0.364
Educationa
    <10 yr -217.33 (-695.297–260.647) 0.366 -477.20 (-942.076 –-12.329) 0.044
    = 10 yr 6.97 (-523.010–536.940) 0.979 38.34 (-295.850–372.538) 0.820
    >10 yr (“Abitur”, qualification for the university) 20.14 (-485.440–525.725) 0.937 196.84 (-226.431–620.120) 0.358
    Other -1351.47 (-2485.301 –-217.637) 0.020** -667.82 (-1455.558–119.918) 0.096
Self-efficacy 3.09 (-11.468–17.648) 0.672 -16.30 (-27.881 –-4.713) 0.006**
Open in a new tab

β regression coefficient, CI confidence interval

**p<0.01

*p<0.05

a reference: high school degree.

Discussion

The results of this analysis show that the levels of light, moderate and overall PA in our sample of older people are high. 72.8% of the male and 79.7% of the female study participants fulfilled the age specific WHO-recommendations for moderate PA for people aged over 65 years (≥150 minutes moderate PA or ≥75 minutes vigorous PA). Especially female participants in the age group +80 years were above average physically active in people in the same age [14], all women in this age group (n = 6) reached the recommendations for moderate PA.

The study results show that age is a significant negative determinant for moderate and overall PA in men. It can be concluded that men become more physically inactive with age. This is in contrast to other studies in which women are generally less physically active than their male counterparts [3, 14, 33]. This finding can also be explained by the fact that women in our sample in particular were physically active to an above-average extent.

Education was found to be a significant positive determinant regarding moderate PA in men and women and overall PA in men. These results are consistent with other studies. People with higher education tend to be more physically active and generally pay more attention to their personal health [30, 33].

BMI was found to be a significant determinant for moderate PA in women. Additionally, higher BMI in men and women was statistically significantly associated with a decreasing level of overall PA. Self efficacy was a significant negative determinant for PI only in women. Thus, females with higher self efficacy showed lower levels of PI. There was no systematic difference between the gender. Self efficacy was the only statistically significant predictor for PI. Thus, motivation can be seen as an important factor to reduce PI.

Our study participants spent most of their waking time in PI, men spent 68.9% and female 67.1% per day in PI, which correspondends to results from other studies examining the same age group (65% - 80%) [29, 48]. In industrialized countries, high PI values are therefore associated with an enormous burden on health systems [23].

The participants showed a very good adherence in wearing the accelerometer device. The mean wearing time per day was 14 hours. Only 22 of 225 participants had to be excluded from the analysis because of insufficient wearing time (<4 days and <10h per day). Thus, the intervention in our study can be seen as feasible and practicable for this age group.

The public health relevance of PA and PI is high. Due to the demographic changes, maintaining high levels of PA over older ages will become even more important. Regular PA is positively associated with several physical health outcomes. Besides that, high levels of PA have also the potential to maintain cognitive health over the long term into higher ages [8, 9]. In general, high levels of PA can reduce incidence of dementia and cognitive restrictions significantly [49]. Norton et al. has shown that changeable risk factors such as PA and PI are responsible for around a third of Alzheimer's disease worldwide. In Europe, USA and UK physical inactivity was the strongest risk factor [28].

Effective and practicable strategies to increase PA and decrease PI are needed especially for the elderly. A report from WHO for the European Region mentions specific suggestions to increase peoples physical activity (e.g. by promotion of green spaces or cycle paths etc.) [39]. Therefore, to design, scale, and implement effective non-communicable disease prevention programmes, accurate and valid data on physical activity levels and on sedentary behavior are needed as well as valid knowledge about significant determinants of both PA and PI.

Regular PA is a highly effective health-promoting method with strong evidence [2, 4, 23, 25]. International recommendations for PA are existing for all age groups [10, 50], but it still remains under debate how people should accumulate their recommended time of PA over the week [51].

In recent years, the number of interventions targeting PI has increased. However, there is still a lack of recommendations for PI [52]. In addition, it is also not conclusively established whether PI is an independent risk factor for chronic diseases [52]. In summary, recent literature points out that public health activities should emphasise increasing PA at any intensity especially in the elderly [53].

Limitations

This analysis is based on a convenience sample of participants. We used a variety of sampling methods, including the possibility of self-recruitment, some of which have likely increased the proportion of participants with above average PA compared to PA at the population level. We observed high levels of PA, particularly among the older age groups and females which indicate some selection bias.

In general, the use of accelerometer devices for objective assessment of PA allows a valid and reliable record of PA intensity, frequency, and duration. But data from the accelerometer device can potentially differ from the real levels of PA and PI especially in the elderly because several activities are carried out in standing (e.g. gardening) or sedentary positions (e.g. gymnastic on stools) which, as a consequence, can not be assessed reliably.

In general, the explanatory value for both PA models are acceptable. The explanatory value for our PI model is low. Thus, research should focus on further environmental and interpersonal factors, e.g the walkability of neigbourhoods and attractive activities for seniors.

Conclusion

The number of guidelines and recommendations in PA and PI increases continuously. However, there are still certain aspects especially of PI which can not described in an accurate way. To our knowledge we still know only little about the independent negative health effects of sedentary behavior. In general, there is international consensus regarding recommendations for PA but recommendations about PI are still under debate.

Because of demographic change and the associated increase of proportion of older people the need of global prevention strategies is high, simultaneously knowledge to improve our understanding of PA and PI in the elderly. Prevalence of dementia and chronic diseases (e.g. CVD) will probably increase in the next decades. Therefore, the relevance of modifiable risk factors as preventive measures will rise [49].

Our analyses confirm, that especially individual factors (e.g. age, sex) has the largest impact on PA. The results for PI are less clear. Thus, PI may have other predictors. Another important finding of this study is that PA and PI can be seen as mostly independent factors of activity. Participants with a high level of PA showed also high levels of PI. Further research should pay more attention to effective predictors for the reduction of PI and should focus more on environmental and interpersonal factors especially in PI.

Supporting information

S1 File. Dataset.

(XLSX)

Click here for additional data file. (105.2KB, xlsx)

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The study was funded by the Federal Ministry of Education and Research (BMBF) as a site project of the German Centre for Cardiovascular Research (DZHK) (funding sign: 81Z7400174). The funders have had no influence on the conceptualization and conduct of the study and will not have any role in the data analysis and publication of the results.

References

Decision Letter 0

Martin Senechal

7 Jan 2020

PONE-D-19-30791

Levels of and determinants for physical activity and physical inactivity in a group of healthy elderly people in Germany: Baseline results of the MOVING-study

PLOS ONE

Dear Mr Kleinke,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewer #1: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

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Reviewer #1: Yes

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Reviewer #1: The manuscript addresses an interesting topic. The data are unique and the statistical modelling employed, though rather standard, is sound. The results are promising, but there are a number of major concerncs which must be addressed to ensure the reliability of the results.

1. I really appreaciate that the data are attached to the manuscript. This allows me to replicate some of the analysis. However, the number of observations in the .xls file is different from the one reported in the main text. This is a major issue as it is rather unclear which is tha database used for the final analysis. Please, amend the data accordingly.

2. The use of multiple regression is rather sound, though more sophisticated methods could be used. My major concer is about the obtained inferential results. The regression model, as well as any other parametric test, must fulfill some crucial assumptions. For the regression model, you should refer to the Gauss-Markov theorem. If any of the model's assumptions are not fulfilled, then the results are non reliable and modifications of the basic model must be provided. A clear and deep analysis of the residuals must be included. Heteroskedasticity, outlying observations, skewness and heavy-tails may strongly affect your results, making model inference completely misleading. From a simple graphical inspection of the residuals, all these aspects can be easily detected.

3. The goodness-of-fit of the defined regressions is rather poor. This limits the use of those models for the prediction of the response variable, given the covariates. Please, investigate further model specifications, may be including interaction terms and/or latent variables. A more detailed descriptive and graphical analysis of the data may reveal interesting relationshios between the response and the covariates.

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PLoS One. 2020 Aug 13;15(8):e0237495. doi: 10.1371/journal.pone.0237495.r002

Author response to Decision Letter 0

20 Feb 2020

Please see graphics attached in the uploaded file "Response to Reviewers"

File Inventory Manuscript Number: PONE-D-19-30791

Reviewer note #1

The manuscript addresses an interesting topic. The data are unique and the statistical modelling employed, though rather standard, is sound. The results are promising, but there are a number of major concerncs which must be addressed to ensure the reliability of the results.

I really appreaciate that the data are attached to the manuscript. This allows me to replicate some of the analysis. However, the number of observations in the .xls file is different from the one reported in the main text. This is a major issue as it is rather unclear which is tha database used for the final analysis. Please, amend the data accordingly.

Anwser #1

Thank you very much for your constructive comments to the manuscript. In our opinion, your comments will help to increase the quality of the manuscript, especially the part of the statistical analyses.

First, the attached data file included all study participants (n=221) without excluding participants who do not meet the inclusion criteria (n=22). We removed these participants from the attached file. The updated file contains data of n=199 participants. We calculated our results with n=199 participants.

__________________________________________

Reviewer note #2

2. The use of multiple regression is rather sound, though more sophisticated methods could be used. My major concer is about the obtained inferential results. The regression model, as well as any other parametric test, must fulfill some crucial assumptions.

For the regression model, you should refer to the Gauss-Markov theorem. If any of the model's assumptions are not fulfilled, then the results are non reliable and modifications of the basic model must be provided. A clear and deep analysis of the residuals must be included. Heteroscedasticity, outlying observations, skewness and heavy-tails may strongly affect your results, making model inference completely misleading. From a simple graphical inspection of the residuals, all these aspects can be easily detected.

Answer #2

Thank you for your constructive comment to the analyses of the residuals.

For the regression model, we added the reference to the Gauss-Markov theorem. We analyzed the residuals. You can find the residuals in the attached dataset “S1_dataset.xlsx” (variables: RES_1_overall_PA, PRE_1_overall_PA, RES_1_moderate_PA, PRE_1_moderate_PA, RES_1_PI, PRE_1_PI).

We included a detailed analysis of the residuals. First, we graphically analyzed the residuals (see attachments). We came to the result, that all tested residuals are normally distributed. The attached graphics and g-g-plots show that all tested residuals are between range -3 and 3 SD. In addition, no systematic patterns or only very light deviations were detected. Please see also graphical inspection of the residuals: 

GRAPHIS (see attachments)

In addition to the graphical verification, we analyzed the distribution of the residuals also with the Shapiro-Wilk-Test. In these analyses, we came to the same results. All tested residuals are (nearly) normally distributed. Furthermore, literature points out that a small deviation of the normal distribution in case of linear regression is acceptable, especially in bigger samples (Lumley et al. 2002), Eid, 2010). Even if the residuals are not unequivocally normally distributed, this does not lead to a distortion of the regression coefficient (Eid, 2010). In summary, the analyses showed that the assumption of homoscedasticity is given. No systematic deviations, strong skewness or heavy tails were detected.

In principle, an uneven distribution of the residuals can lead to a distortion of the standard error, which possibly can distort the significance test. To verify the results of the significance test, we calculated a bootstrapped regression model (Field, 2008). We performed a bootstrapped regression with 1000 repetitions (Chernick, 2008). In these analyses, we came to the same results: the same predictors became significant. In sum, the results of our multiple linear regression models are reliable.

In addition, we performed Durbin-Watson test to check uncorrelation of the residuals. The results of the Durbin-Watson test should be close to 2. By checking the uncorrelation it was found that this requirement is fulfilled. Additionally, we tested multicollinearity between the variables (results should be smaller than 5) and analyzed the condition index. Results of the condition index should be smaller than 30. In summary, all requirements are fulfilled and results of the linear regression models are reliable.

We added the following text to the manuscript (page 9): “Referring to the Gauß-Markov-Theorem we analyzed the residuals and requirements of multiple linear regression like distribution and homoscedasticity”.

__________________________________________

Reviewer note #3

The goodness-of-fit of the defined regressions is rather poor. This limits the use of those models for the prediction of the response variable, given the covariates. Please, investigate further model specifications, may be including interaction terms and/or latent variables. A more detailed descriptive and graphical analysis of the data may reveal interesting relationshios between the response and the covariates.

Answer #3

Thank you very much for this comment.

The chosen predictors explain up to 30% of the variance of the model (table 6). Only the model of physical inactivity (PI) shows an unsatisfactory explanatory value for male participants (R2=0.03). Obviously, this is because other predictors, especially in men, affect physical inactivity. This result is in line with current research, that physical activity and physical inactivity can be seen as independent factors (Stamatakis, 2018). Furthermore, this study examines human behavior that is often influenced by other, not measurable factors. In analyzes of human behavior, lower R2 values (>50%) can be expected.

We defined possible determinants for PA and PI based on literature analysis and hypothesis. A priori, no interaction terms were defined. In our opinion, a retrospective examination of interaction terms is not purposeful. In case of retrospective examination, there is a possibility that results are random and not based on hypothesizes. In conclusion, only a limited interpretation of these results would be possible. However, we are grateful for this note and we will consider this aspect for future analyses.

 

References

Eid, M., Gollwitzer, M. & Schmitt, M. (2010). Statistik und Forschungsmethoden. Lehrbuch. Mit Online-Materialien. Weinheim: Beltz.

Cohen, J. (1988). Statisitcal power analyses for behavioral sciences (2nd ed). Hillsdale, NJ: Erlbaum Associates.

Field, A. (2009). Discovering statistics using SPSS (3rd ed). London, SAGE

Michael R. Chernick (2008) Bootstrap methods: a guide for practioners and researchers, 2. Auflage, Hoboken, NJ: Wiley.

Lumley, Thomas, Paula Diehr, Scott Emerson, and Lu Chen. 2002. “The Importance of the Normality Assumption in Large Public Health Data Sets.” Annual Review of Public Health 23(1):151–69.

Decision Letter 1

Thalia Fernandez

8 Jun 2020

PONE-D-19-30791R1

Levels of and determinants for physical activity and physical inactivity in a group of healthy elderly people in Germany: Baseline results of the MOVING-study

PLOS ONE

Dear Dr. Kleinke,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 23 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Thalia Fernandez, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Dear Dr Kleinke,

Although the reviewer who viewed the original manuscript says that the article must be accepted, I sent the revised manuscript to another reviewer, and she found some details. I agree with her that the discussion section should include more interpretive work.

I also think that phrases such as "Overall, women were slightly more physically active than male participants (mean 1,929.4 min 228 and 1,821.2 min per week, respectively)" include some degree of imprecision because the authors did not use any statistical analysis to make these comparisons; therefore, they found no significant differences (in this case, between genders). I recommend using statistical analyzes of comparison of means (and consider the multiplicity of tests to adjust its level of significance).

Perhaps the Introduction, and especially the Discussion, could be enriched if the physical activity were considered as one of the proxies of the Cognitive Reserve (Stern, 2009). Aging is not only related to cardiovascular disease; aging is the principal risk-factor to MCI and dementia. Therefore, I believe that the authors will agree that, in aging, it is not only desirable that the cardiovascular system remains healthy, but also cognitive functions.

Also, author should check the abbreviation for "minutes".

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Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: (No Response)

Reviewer #2: Partly

**********

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Reviewer #1: (No Response)

Reviewer #2: I Don't Know

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Reviewer #1: (No Response)

Reviewer #2: Yes

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Reviewer #1: (No Response)

Reviewer #2: The manuscript has problems in organizing the topics in introduction section. Information is missing in the material and methods section and Discussion section needs more interpretative work.

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Attachment

Submitted filename: Comments to the author.doc

Click here for additional data file. (36KB, doc)
PLoS One. 2020 Aug 13;15(8):e0237495. doi: 10.1371/journal.pone.0237495.r004

Author response to Decision Letter 1

22 Jun 2020

Abstract

1. This line is confusing, rewrite using the terms light, moderate and vigorous

“Insufficient physical activity (PA) and high levels of physical inactivity (PI)”

Thank you for this comment. In order to avoid confusions and to structure the statement more clearly, we have adapted the sentence. “Low levels of PA and high levels of physical inactivity (PI) are associated with higher mortality and cardiovascular diseases.”

Introduction

1. The introduction does not have a clear structure. I suggest the following structure:

Manuscript

• Effect of insufficient PA

• Definition PI or sedentary behavior

• Effect of insufficient PA and PI

• Effect of regular PA

• General prevalencia (disease)

• Recomendations (moderate PA)

• Global prevalence (recommendations moderate PA)

• Germany prevalence (recommendations moderate PA)

• Prevalence sedentary

• Factors affecting amount of PA and PI

• Prevention

Suggestion

• Definition of physical activity (PA)

• Effect of regular PA

• Recommendations (moderate PA)

• General prevalence (recommendations moderate PA)

• Germany prevalence (recommendations moderate PA)

• General prevalencia (disease)

• Definition PI or sedentary behavior

• Effect of insufficient PA and PI

• Prevalence sedentary

• Factors affecting amount of PA and PI

• Prevention

Thank you for your comment to the structure of the introduction. We totally accepted your comment and structured the introduction section accordingly your suggestion. We marked all changes with yellow color. Now, the introduction part is structured more clearly. We rebuilt the introduction section, restructured, and separated some parts and sentences. Therefore, it might be laborious to track all the changes we have made.

2. The use of definitions is not clear, please use light PA, moderate PA and vigorous PA or low or high levels of PA in the introduction section.

We tried to make the introduction section easier to understand by using the terminology low and high levels of PA and PI and by following your recommendations to structure. We changed all relevant parts in the introduction section (for example lines 64, 90 or 97).

3. Explained in detail the background studies in introduction section.

“Overweight and obesity (BMI>30 Kg/m2) have an negative influence on the level of PA and PI in the elderly. Higher education has a positive influence on PA. Further factors that influence the amount of PA and PI are marital status, income, wellbeing, psychosocial variables such as self-efficacy, and social and cultural parameters (29,30,35,36)

In this section, we report which determinants have an influence on the amount of PA and PI. We added further background information to the referenced studies. For example, please see lines 115-119: “Overweight and obesity (BMI>30 kg/m2) have a negative influence on the level of PA and PI in the elderly. In a cross-sectional study, in which 15,239 subjects were surveyed from 15 member states of the European Union, it was found, that people with a low BMI (<20 kg/m2) and normal BMI (20-25 kg/m2) have low prevalence in PI (both genders). In contrast, people with a BMI above 30 kg/m2 showed a more sedentary lifestyle (30,36).”

A report from the WHO about the prevention of non-communicable-diseases (NCD) in south eastern European countries showed that the promotion of PA and decrease reduction of PI are key aspects in public health efforts (37)”

We expanded information about this topic and added the following sentences to the introduction section (see lines 128-131): “Promoting physical activity through mass media was a primary goal for immediate action (38). In addition, the Global Strategy on Diet, Physical Activity on Health (25) and the European Charter on Counteracting Obesity (39) underline the relevance of PA to fight against obesity.”

Materials and Methods

1. Rewrite the following paragraph including the variables considered in the regression, independent and dependent variables.

“Descriptive statistics were used to describe the population with respect to the levels of PA and PI. To identify determinants of PA and PI, multiple linear regression models were calculated. All variables were checked for multicollinearity using a correlation (Pearson)”

Thank you for this comment to the method section. We added the following sentences to the manuscript (lines 216-218): “Dependent variables for the regression models were overall and moderate PA as well as PI. The effect of the independent variables age, BMI, education and self-efficacy was examined for all regression models. All independent variables were checked for multicollinearity using a correlation (Pearson). ”

Discussion

1-Organize the discussion section based on levels of PA.

Discussion section requires more work; in particular, you must interpret the models you obtained from each regression, then you will be able to compare your interpretations with previous studies.

Thank you for your comment to the structure of the discussion section. To address your suggestion, we restructured some parts in the discussion based on intensity of PA levels (moderate PA, overall PA and PI). Therefore, we rebuilt the discussion section, restructured, and separated some parts and sentences (please see yellow marks).

In addition, we followed your recommendation and expanded the discussion section on interpretation and information from other studies. For example (lines 290-294): “The study results show that age is a significant negative determinant for moderate and overall PA in men. It can be concluded that men become more physically inactive with age. This is in contrast to other studies in which women are generally less physically active than their male counterparts (3,14,33). This finding can also be explained by the fact that women in our sample in particular were physically active to an above-average extent.”

2. Why is this relevant to the discussion?, this is information should be included in material and methods section.

“The participants showed a very good adherence in wearing the accelerometer device. The mean wearing time per day was 14 hours. Only 22 of 225 participants had to be excluded from the analysis because of insufficient wearing time”

Thank you for your comment. In our opinion, the finding about the wearing time is important and relevant. In addition to the effect, the feasibility and practicability of the intervention are important for the success of the study. The good wearing time of the accelerometer device shows, that such interventions are suitable for older people. This means that further studies can be carried out using the same method without major concerns or adjustments in methods. Therefore, we added the following sentence to the manuscript (see lines 311-312): “Thus, the intervention in our study can be seen as feasible and practicable for this age group.”

3. Did you make any intervention in this study? Your participants got feedback about their PA, but you do not have a control measure before wear the accelerometer. I think this paragraph should be eliminated.

“In recent years, the number of interventions targeting PI has increased. However, there is still a lack of recommendations for PI (49). In addition, it is also not conclusively established whether PI is an independent risk factor for chronic diseases (49). In summary, recent literature points out that public health activities should emphasise increasing PA at any intensity especially in the elderly. (50)”

This section primary refers to the theoretical background and is not related to results of our intervention or study. Although number of interventions in PI are increasing in general, there are still no global recommendations for PI. Therefore, we would like to keep this section in the manuscript as an overall view about the topic and relevance.

Minor suggestions:

Thank you for carefully reviewing our manuscript. We implemented all minor comments.

Doble parentheses líne 63

Misplaced reference line 66 (7,8)

Error in parentheses líne 69

Doble parentheses line109

Error in word “therof” líne 221

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Decision Letter 2

Thalia Fernandez

29 Jul 2020

Levels of and determinants for physical activity and physical inactivity in a group of healthy elderly people in Germany: Baseline results of the MOVING-study

PONE-D-19-30791R2

Dear Dr. Kleinke,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Thalia Fernandez, Ph.D.

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #1: (No Response)

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: (No Response)

Reviewer #2: Yes

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Reviewer #1: (No Response)

Reviewer #2: The authors have made a great effort to follow the recomendations improving the structure and content of the paper, however the discussion section needs more work. They have described the factos that are associates with increased physical activity, I would expect in discussion section an explanation about why these factors and no others affect the physical activity.

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Reviewer #1: No

Reviewer #2: No

Acceptance letter

Thalia Fernandez

4 Aug 2020

PONE-D-19-30791R2

Levels of and determinants for physical activity and physical inactivity in a group of healthy elderly people in Germany: Baseline results of the MOVING-study

Dear Dr. Kleinke:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Dr. Thalia Fernandez

Academic Editor

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