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In Studies I and II, information on PA was derived from the questionnaires from 1975 and 1981. Both the intensity of PA and the volume of PA were assessed. Information on the intensity of PA was derived from the following question:

Is your physical activity during leisure-time about as strenuous on average as: 1) walking

2) alternately walking and jogging 3) jogging

4) running

9LJRURXV3$ZDVGH¿QHGDVFKRRVLQJRQHRIWKHRSWLRQV±.XMDODHWDO Information on the volume of PA was based on the following questions:

How long does the physical activity last at one session on average: a) less than 15 minutes (class midpoint 7.5 min)

b) 15 min – less than 30 min (22.5 min) c) 30 min – less than 1 hour (45 min) d) 1 hour – less than 2 hours (90 min) e) over two hours (120 min)

Presently how many times per month do you engage in physical activity during your leisure time:

a) less than once a month (class midpoint 0.5) b) 1-2 times per month (1.5)

c) 3-5 times per month (4) d) 6-10 times per month (8) e) 11-19 times per month (15)

f) more than 20 times per month (20)

For the intensity of a session, the same question was used as for vigorous activity, but the following MET values were included for the choices:

a) walking (corresponding to 4 METs) b) alternately walking and jogging (6 METs) c) jogging (10 METs)

How much of your daily journey to work is spent in walking, cycling, running and/or cross-country skiing?

a) less than 15 min (class midpoint 7 min) b) 15 min – less than a half an hour (22 min) c) half an hour to less than an hour (45 min) d) an hour or more (75 min)

e) I am presently not at work (0 min)

On the basis of these questions, the MET (metabolic equivalent that is a multiple of metabolic resting energy expenditure) score was calculated using the following formula: frequency x duration x intensity. The MET score was calculated for both LTPA and for PA during work journeys using the class midpoints, and these were subsequently added together (Kujala et al., 1998). The MET value 4 corresponding to walking was used in the calculation for PA during work journeys. Practically expressed, 1 MET h/day corresponds to 30 minutes of slow walking every other day. The MET score has been validated in the older cohort of the Finnish Twin Cohort Study (Waller et al., 2008).

 7RUHÀHFWWKHORQJWHUPOHYHORI3$.XMDODHWDOFRPSRXQGYDULDEOHV combining the PA level from both questionnaires were created and used in the analyses. For the intensity of PA, three categories were created: 2. No vigorous PA in either of the years 1975 or 1981 (inactive according to

long-term vigorous physical activity, i.e., VLinactive)

3. Vigorous PA in both years 1975 and 1981 (active according to long-term vigorous physical activity, i.e., VLactive)

Change (from no vigorous PA in 1975 to vigorous PA in 1981 or from vigorous PA in 1975 to no vigorous PA in 1981, change in long-term vigorous activity, i.e., VLchange)

The participants who dropped out of vigorous PA and who picked up vigorous PA between the years 1975 and 1981 were integrated to reach greater statistical SRZHUEHFDXVHQRGLɣHUHQFHEHWZHHQWKHVHJURXSVLQSUHOLPLQDU\DQDO\VHVZDVVHHQ  )RUWKHORQJWHUPYROXPHRI3$WZRGLɣHUHQWFRPSRXQGYDULDEOHVZHUH created. First, the mean MET score from the questionnaires from 1975 and 1981 was calculated. Second, a compound variable to discern the most inactive individuals was created. The categorization was the following: 2. The participants who belonged to the most inactive MET quintile (I) in both

years 1975 and 1981 (inactive according to long-term quantitative physical activity, i.e., QLinactive)

3. The participants who belonged to other quintiles (II–V) than the most inactive quintile (I) in both years 1975 and 1981 (active according to long- term quantitative physical activity, i.e., QLactive)

Change (from the most inactive quintile (I) in 1975 to other quintile (II–V) in 1981 or from an active quintile (II–V) in 1975 to the most inactive quintile (I) in 1981, change in long-term quantitative activity, i.e., QLchange)

In Study III, accelerometers were used in evaluating PA levels. The accelerometer type was the hip-worn Hookie AM20 (Traxmeet Ltd, Espoo) that uses a light digital triaxial acceleration sensor (ADXL345; Analog Services, Norwood, MA, USA). The device stored the raw acceleration signals with 100 Hz sampling frequency. The raw acceleration data was turned into the mean amplitude deviation (MAD) values independent of accelerometer brand (Vähä-Ypyä et al., 2015a). The MAD value describes the resultant acceleration during a 6-second (s) epoch. The MAD values are strongly correlated with oxygen consumption (VO2) (Vähä-Ypyä et al., 2015b) and can be converted into MET values. The posture in stationary activities was determined using the angle for posture estimation (APE) (Vähä-Ypyä et al., 2018). The participants were instructed to wear the accelerometer during all waking hours but to remove the device when entering a sauna, shower, or swimming pool. The participant’s recording was included when it had at least 10 hours of data from at least 4 days. Time periods over 30 minutes without any acceleration signal have been interpreted as “non-wearing time”. On average, the accelerometer monitoring was done 3.5 weeks (standard deviation (SD) 5.5) after the cognition interviews. The dimensions of PA and SB examined in this study were: SB including sitting and lying but not standing (SB), LPA (1.5–3 METs), MVPA (over 3 METs), daily step count, and the mean daily MET. The mean daily MET is the average MET of all waking hours.

4.2.2 DEMENTIA MORTALITY AND COGNITION

Dementia mortality was followed for those participants who had answered both the questionnaire in 1975 and in 1981, providing the required information to calculate the MET score. The follow-up period ranged from 1981 to the end of the year 2011. The causes of death and exact dates of death and emigration were acquired IURPWKH&DXVHRI'HDWK5HJLVWHU'HPHQWLDPRUWDOLW\ZDVGH¿QHGDFFRUGLQJWR WKHFRQWHPSRUDU\,&'FODVVL¿FDWLRQ7KHIROORZLQJFDWHJRULHVZHUHFODVVL¿HGDV dementia deaths: in ICD-8: category 290 (Senile and presenile organic psychotic conditions) and 794 (Senility without mention of psychosis); in ICD-9: category 290 (Senile and presenile organic psychotic conditions), 3310A (Alzheimer’s disease), 3311A (Pick’s disease), 4378A (Multi-infarct dementia), and 797 (Senility

without mention of psychosis); and in ICD-10: category F01 (Vascular dementia), )1RQVSHFL¿FGHPHQWLD*$O]KHLPHU¶VGLVHDVH*&HUHEUDODWURSK\ including Pick’s disease), and R54 (Senility). The contributing causes of death were not considered.

The telephone cognition screening included two telephone screening instruments: Telephone assessment of dementia (TELE) and Telephone Interview of Cognitive Status (TICS). The interviews were carried out by experienced trained research nurses and a PhD student from the Department of Public Health at the University of Turku. Notwithstanding the converging questions of TELE and TICS, the telephone cognition screening interview formed a 29-item linear variable representing global cognition (the total cognitive score) with higher points indicating better cognition. The maximum scores for TELE and TICS are 20 and 33, respectively. Higher SRLQWVLQGLFDWHEHWWHUFRJQLWLYHIXQFWLRQ7KHFXWRɣVFRUHVIRUFRJQLWLYHLPSDLUPHQW ZHUHIRU7(/(DQGIRU7,&67KHFXWRɣVFRUHVIRUKHDOWK\FRJQLWLRQZHUH >17.5 for TELE and >26.5 for TICS (Laitala et al., 2008). The intermediate scores 16–17.5 for TELE and 22.5–26.5 for TICS and scores for those whose TELE and TICS scores are in disagreement were designated MCI (Vuoksimaa et al., 2016), although it has to be taken into account that our cognitive screening instruments are not validated for MCI.

4.2.3 COVARIATES

Data on basic demographics included age (at the time of the questionnaire in 1981 or at the time of the cognition interview), sex, and zygosity. For the vast majority, zygosity (MZ or DZ) had been ascertained with a validated questionnaire, but there were also some with unknown zygosity (incoherence in the validated questionnaire of zygosity).

In Study I and II, the additional covariates used were education, BMI, heavy drinking, hypertension, smoking, and for Study II, also the length of follow-up. Excluding the length of follow-up, all of these are self-reports, mainly from the 1981 questionnaire. Education was described in years of schooling ranging from 3 to 16 years (Silventoinen et al., 2000) and the information was based on the 1975 questionnaire if missing in the 1981 questionnaire. BMI was calculated from self-reported height and weight from the 1981 questionnaire. Heavy drinking was designated as consuming at least once a month and on a single occasion, more WKDQ¿YHEHHUVDERWWOHRIZLQHRUDKDOIERWWOHRIVSLULWV.DSULRHWDO 7KLVGH¿QLWLRQLVYHU\QHDUWKHGH¿QLWLRQIURPWKH1DWLRQDO,QVWLWXWHVRI+HDOWK GH¿QLQJELQJHGULQNLQJDVGULQNLQJ³RUPRUHPDOHVRURUPRUHIHPDOHVGULQNV containing any kind of alcohol within a two-hour period” (National Institutes of Health, 2003). Hypertension was used as a binary variable based on questionnaire

information on reporting “permanently elevated blood pressure and medication for it” and on information on hypertension mediation provided by the Social Insurance ,QVWLWXWLRQRI)LQODQG.XMDODHWDO6PRNLQJVWDWXVZDVFODVVL¿HGIURPD detailed smoking history questionnaire and used as a four-category variable (i.e., “never smoked”, “occasional smoker”, “former smoker”, “current smoker”) (Kaprio et al., 1988). For smoking status, also, the information on smoking was used from the 1975 questionnaire if it was absent in the 1981 questionnaire. In Study I, the length of follow-up was incorporated in the model (Cox regression analysis) and in Study II, it was calculated as the interval between the 1981 questionnaire and the cognition screening interview and used as a covariate in the regression model (logistic or linear regression model).

In Study III, the covariates were age, education, accelerometer wearing time, BMI, and living condition. The accelerometer wearing time was used as a covariate when studying SB and the mean daily MET. BMI was calculated from the height and weight reported in the questionnaire which was sent to the participants in conjunction with the accelerometer. Living condition was queried in conjunction with the cognition screening interview. The answers were dichotomized into two categories “living alone” or “living with a spouse, children or grandchildren, relatives, siblings, or other”.

In Studies I and II, the analyses were also done in a subgroup in which all the participants were healthy at baseline. These additional analyses were performed WRHOLPLQDWHFKURQLFGLVHDVHV¶HɣHFWRQ3$7KHPRUHGHWDLOHGGHVFULSWLRQRQWKH exclusion for chronic diseases can be found in an earlier article from the research group (Kujala et al., 1998). In short, the most important chronic diseases including cancer, diabetes, chronic obstructive pulmonary disease, and cardiovascular diseases, notwithstanding hypertension or venous disease, were excluded based on information from the 1981 questionnaire, information on reimbursable medications from the Social Insurance Institution in Finland, and the data received from Finland’s nationwide hospital discharge register (between 1972 and 1982) and from the Finnish Cancer Registry (before 1983).