. Author manuscript; available in PMC: 2012 Sep 1.

Published in final edited form as: J Sci Med Sport. 2011 Apr 27;14(5):404–410. doi: 10.1016/j.jsams.2011.03.013

Table 2. Prediction equations to transform estimates of MVPA from one set of cutpoints into MVPA estimated from another set of cutpoints.

Accelerometer Cutpoint MVPA min•d^-1		Prediction Equations^†							10-fold Cross Validation^§

Outcome Variable^a	Predictor variable	Intercept	MVPA min•d^-1	MVPA min•d^-1 Squared	MVPA min•d^-1 Square Root	Age (years)	Wear time^‡	R²	Average Error (min•d^-1) ^b	Absolute Percent Error ^c
Pate	Puyau	-27.07796	1.061643	-0.001669	10.41079		2.048583	0.88	11.1	11.8%
Pate	Puyau	-3.189774	1.040246	-0.001703	11.16557			0.87	11.3	12.0%

	Freedson	8.180722	.3922986	.0002284			-2.108117	0.75	15.8	17.4%
	Freedson	-40.03466	.5757257	.0001202	-4.559613	12.51481		0.78	14.4	15.8%
	Freedson	-21.6284	.5976948	.0001054	-4.264987	12.82377	-2.174009	0.78	14.3	15.5%

	Sirard	5.152623	1.438485				2.246005	0.72	18.5	19.7%
	Sirard	-138.9017	0.5297728	-0.0001241	14.02476	23.64033	1.849608	0.86	12.5	13.4%
	Sirard	-118.9256	.5321844	-.0001937	14.50605	24.06562		0.84	12.7	13.7%

	van Cauwenberghe	17.07271	1.279121					0.96	5.9	6.4%
	van Cauwenberghe	3.41532	1.265061				1.085201	0.97	5.7	6.3%

Puyau	Pate	6.879354	0.3215901	0.0007953			-0.72323	0.87	6.0	17.2%
	Pate	-1.631061	.3065897	.0008218				0.86	6.1	17.4%

	Freedson	7.401612	0.1408477	0.0001596			-1.31163	0.48	11.9	37.8%
	Freedson	-37.00836	.1004103	.0001946	.3095264	7.062028		0.52	11.5	36.5%
	Freedson	-24.83794	0.1181168	0.0001836	0.3915857	7.261133	-1.355968	0.52	11.4	36.1%

	Sirard	-0.5671588	0.8613491				0.015586	0.76	10.2	27.1%
	Sirard	-75.53115	.4430329	.0006495	4.847966	14.3972		0.91	5.4	17.2%
	Sirard	-75.37523	0.4304032	0.0006766	5.008838	14.40396	-0.0540935	0.91	5.4	17.2%

	van Cauwenberghe	-1.289946	0.5382766	0.0009392				0.95	3.5	10.0%
	van Cauwenberghe	3.560949	0.5481894	0.0009111			-0.3984867	0.95	3.6	10.0%

Freedson	Pate	1.921614	1.9832	-0.0021126			6.569524	0.77	26.7	12.3%

	Pate	77.29174	.2173512	.0002469	26.4726	-23.04593		0.79	25.2	10.3%
	Pate	1.407298	0.1061053	0.0004844	25.92285	-22.50468	6.517857	0.82	23.9	9.6%

	Puyau	19.03004	3.247166	-0.0098703			10.42879	0.55	37.3	18.2%
	Puyau	138.1948	-1.277124	.001129	46.51387	-24.69632		0.55	37.6	16.9%
	Puyau	18.54305	-1.104061	0.0011465	42.00134	-24.00351	10.20374	0.61	33.3	15.8%

	Sirard	143.7481	3.14342	-.0077486				0.56	37.6	18.8%
	Sirard	17.76059	2.868255	-0.0067386			10.10434	0.62	35.3	17.3%

	van Cauwenberghe	9.197958	2.546966	-0.0047027			8.482786	0.67	32.0	15.1%
	van Cauwenberghe	105.8778	-0.284422	0.0004263	35.31705	-24.18957		0.68	31.4	13.1%
	van Cauwenberghe	6.730405	-0.3484064	0.0007365	33.65001	-23.66149	8.386824	0.72	29.6	12.7%

Sirard	Pate	6.972053	0.3998896	0.0007227			-0.74987	0.72	12.0	30.7%
	Pate	53.22646	.2395267	.0009554	2.067009	-14.94238		0.85	8.3	22.6%
	Pate	62.96109	0.2620144	0.0009197	1.96676	-14.97197	-0.7800015	0.85	8.2	22.4%

	Puyau	0.429851	1.143558	-0.0005312			0.132389	0.76	11.8	27.7%
	Puyau	64.64648	.872056	.0001365	2.735017	-16.44998		0.91	6.2	17.1%
	Puyau	63.49087	0.876299	0.0001319	2.64906	-16.433	0.1080192	0.91	6.2	17.1%

	Freedson	-4.13197	0.2783899				-1.77046	0.55	13.9	38.4%
	Freedson	20.65111	-.0412707	.0003909	2.618793	-8.375547		0.60	13.2	35.8%
	Freedson	35.89801	-0.0153043	0.0003738	2.637629	-8.126762	-1.654241	0.61	13.0	35.2%

	van Cauwenberghe	0.2340504	0.7760613				-0.4056065	0.76	11.6	27.8%
	van Cauwenberghe	59.95077	0.4970589	0.0010028	1.835858	-15.72771		0.90	6.7	18.9%
	van Cauwenberghe	64.25752	0.4981462	0.0009926	1.931716	-15.75026	-0.371556	0.91	6.7	18.6%

van Cauwenberghe	Pate	-2.02805	0.7584412				-0.664218	0.97	4.4	7.5%
	Pate	-3.508009	0.6216894	0.0005406				0.97	4.3	7.1%

	Puyau	1.883229	1.336341				0.8734971	0.95	5.3	9.4%
	Puyau	13.12951	1.349016					0.95	5.3	9.6%

	Freedson	-6.451813	0.3680809				-1.88836	0.62	14.7	25.8%
	Freedson	-43.16919	0.2943966	0.000191	-1.585303	9.951828		0.66	13.6	23.9%
	Freedson	-23.49861	0.3412859	0.0001607	-1.940271	10.23911	-1.886545	0.67	13.6	24.0%

	Sirard	1.043279	1.175623				0.8132831	0.76	14.1	22.8%
	Sirard	-103.4327	0.5078929	0.0002861	9.528605	19.64146		0.90	7.9	13.6%
	Sirard	-109.6294	0.5173321	0.0002879	9.259545	19.51529	0.5994181	0.90	7.8	13.5%

For example, the prediction of MVPA min•d^-1 from studies using Puyau cutpoints into Pate cutpoints

Average Error calculated as $\sqrt{[\sum {(Y - Y^{'})}^{2} / (N - 1)]}$ where “Y” is the actual value, “Y′” is the predicted value

Absolute percent error calculated as [(Y − Y′)/Y] * 100

^†

Prediction equations developed with the entire sample

^§

Estimates based on the average errors calculated across the 10 replications

^‡

Total accelerometry wear time per day